Systems and methods for multivariate artificial intelligence (ai) smart cards

ABSTRACT

Systems and methods for multivariate Artificial Intelligence (AI) smart cards are provided. An AI smart card may include, for example, pre-stored policy data that may be utilized as a portion of multivariate input by a suite of AI modules to formulate and analyze a claim of loss.

COPYRIGHT NOTICE

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BACKGROUND

There are currently two (2) basic manners in which customer interactionsrequiring complex decision making may be carried out. First, a customermay conduct live telephone or online chat conversations with a humanCustomer Service Representative (CSR). These conversations are oftenenhanced and/or preceded by utilization of an Interactive Voice Response(IVR) system to triage and/or route the customer's query. Unfortunately,as human staffing is an expensive resource, many customers mayexperience long wait times and/or poor quality of service. Long waittimes, in particular, often dissuade customers from becoming involvedwith the decision-making process and/or delay the process. In caseswhere timely information is important for result determination, suchdelays often lead to mistakes and associated economic losses.

In the second manner of customer interactions, advancements insmartphone technology have provided for mobile platforms via whichcustomers may skip human-to-human interactions (and the attendant queuesthereof) by submitting queries via mobile applications. While thepromise of such a technology-enhanced option was originally high, it hasfailed to remedy the problems with the query process. Smartphoneapplications must be coded to provide menus and specific questions tocustomers so that they may be properly guided through the process, forexample. However, even leveraging the best marketing research has failedto generate menus and questions that substantial portions of a customerbase find intuitive. Confusion with mobile device applications increasesthe frequency of incorrect data entry, which, in turn, leads toinaccurate decision-making results. Inaccurate results cause economicerrors which do harm to either or both of the customer and the entity towhich the customer has submitted a query.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures depict embodiments for purposes of illustration only. Oneskilled in the art will readily recognize from the following descriptionthat alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles describedherein, wherein:

FIG. 1 is a block diagram of a system according to some embodiments;

FIG. 2 is a mixed block and perspective diagram of a system according tosome embodiments;

FIG. 3A is diagram of a smart card according to some embodiments;

FIG. 3B is diagram of a barcode according to some embodiments;

FIG. 4 is a systemic flow diagram of a method according to someembodiments;

FIG. 5 is a flow diagram of a method according to some embodiments;

FIG. 6 is a systemic flow diagram of a method according to someembodiments;

FIG. 7A, FIG. 7B, FIG. 7C, and FIG. 7D are diagrams of a systemproviding example interfaces according to some embodiments;

FIG. 8 is a block diagram of an apparatus according to some embodiments;and

FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, and FIG. 9E are perspective diagramsof exemplary data storage devices according to some embodiments.

DETAILED DESCRIPTION I. Introduction

Complex decision-making results based on customer (and/or client orpotential customer/client) queries continue to be a bottleneck and asource of error despite leveraging of mobile device applications toreduce queue times for CSR conversations. Accordingly, entities taskedwith providing results based on such queries must either increase CSRstaffing, which greatly increases costs, or spend large amounts ofcapital developing new menus and questions for mobile deviceapplications, with no guarantee of success in providing more intuitiveand less error-prone interfaces.

In accordance with embodiments herein, these and other deficiencies ofprevious solutions are remedied by providing systems, apparatus,methods, and articles of manufacture for utilizing multivariateArtificial Intelligence (AI) smart cards. Some embodiments, for example,utilize smart cards to enhance the speed and accuracy of initial orFirst Notice Of Loss (FNOL) inputs and then leverage multi-tiered AIanalysis to automatically process the inputs to generate an accurate andtimely result. In some embodiments, the AI analysis may accept aplurality of inputs such as (i) data from and/or based upon receivedsmart card data (e.g., metadata), (ii) customer response language data(e.g., natural spoken and/or written language data), and/or (iii)customer device sensor data (e.g., pictures, video, readings, etc.) andmay convert each input into a numeric value. According to someembodiments, the numeric values may be combined (e.g., in accordancewith one or more mathematical operations) and evaluated by stored AIlogic rules to identify one or more statistically relevant matches withprestored data. In some embodiments, the prestored matches may beutilized to calculate or compute a result for the customer's query(e.g., automatically—without human input into the analysis procedure).

II. Multivariate AI Smart Card Systems

Referring first to FIG. 1, a block diagram of a system 100 according tosome embodiments is shown. In some embodiments, the system 100 maycomprise a plurality of user devices 102 a-n, a network 104, a smartcard 106 (e.g., a third-party device), a controller device 110, and/or adatabase 140. As depicted in FIG. 1, any or all of the devices 102 a-n,106, 110, 140 (or any combinations thereof) may be in communication viathe network 104. In some embodiments, the system 100 may be utilized toprovide for more timely and more accurate complex, multivariatedecision-making results for customer (or potential customer) queries.The controller device 110 may, for example, interface with one or moreof the user devices 102 a-n and/or the smart card 106 to retrievemultivariate data (e.g., via a specially structured interface output byeach user device 102 a-n; not separately shown) to process a result fora user-initiated query utilizing multi-step AI logical processing.

Fewer or more components 102 a-n, 104, 106, 110, 140 and/or variousconfigurations of the depicted components 102 a-n, 104, 106, 110, 140may be included in the production environment system 100 withoutdeviating from the scope of embodiments described herein. In someembodiments, the components 102 a-n, 104, 106, 110, 140 may be similarin configuration and/or functionality to similarly named and/or numberedcomponents as described herein. In some embodiments, the system 100(and/or portion thereof) may comprise a multivariate AI smart cardprogram, system, and/or platform programmed and/or otherwise configuredto execute, conduct, and/or facilitate the methods 400, 500, 600 of FIG.4, FIG. 5, and/or FIG. 6 herein, and/or portions or combinationsthereof.

The user devices 102 a-n, in some embodiments, may comprise any types orconfigurations of computing, mobile electronic, network, user, and/orcommunication devices that are or become known or practicable. The userdevices 102 a-n may, for example, comprise one or more tablet computers,such as an iPad® manufactured by Apple®, Inc. of Cupertino, Calif.,and/or cellular and/or wireless telephones or “smart” phones, such as aniPhone® (also manufactured by Apple®, Inc.) or an Optimus™ S smart phonemanufactured by LG® Electronics, Inc. of San Diego, Calif., and runningthe Android® operating system from Google®, Inc. of Mountain View,Calif. In some embodiments, the user devices 102 a-n may comprisedevices owned and/or operated by one or more users, such as customers,potential customers, contractors, and/or agents. According to someembodiments, the user devices 102 a-n may communicate with thecontroller device 110 via the network 104 to conduct various complexand/or multivariate queries such as underwriting inquiries, e.g., tosubmit a FNOL and/or resolve a claim of loss, as described herein.

In some embodiments, the user devices 102 a-n may interface with thecontroller device 110 to effectuate communications (direct or indirect)with one or more other user devices 102 a-n (such communication notexplicitly shown in FIG. 1) operated by other users, for example. Insome embodiments, the user devices 102 a-n may interface with the smartcard 106 to effectuate communications (direct or indirect) with thecontroller device 110. In some embodiments, the user devices 102 a-nand/or the smart card 106 may comprise one or more sensors and/or otherdata acquisition and/or input devices. In some embodiments, input fromsuch devices 102 a-n, 106 may be provided to the controller device 110to be utilized as input to a multivariate AI processing routine and/orprocess to automatically provide a result to the user, as describedherein.

The network 104 may, according to some embodiments, comprise a LocalArea Network (LAN; wireless and/or wired), cellular telephone,Bluetooth® and/or Bluetooth Low Energy (BLE), Near Field Communication(NFC), and/or Radio Frequency (RF) network with communication linksbetween the controller device 110, the user devices 102 a-n, the smartcard 106, and/or the database 140. In some embodiments, the network 104may comprise direct communications links between any or all of thecomponents 102 a-n, 106, 110, 140 of the system 100. The user devices102 a-n may, for example, be directly interfaced or connected to one ormore of the controller device 110 and/or the smart card 106 via one ormore wires, cables, wireless links, and/or other network components,such network components (e.g., communication links) comprising portionsof the network 104. In some embodiments, the network 104 may compriseone or many other links or network components other than those depictedin FIG. 1. The user devices 102 a-n may, for example, be connected tothe controller device 110 via various cell towers, routers, repeaters,ports, switches, and/or other network components that comprise theInternet and/or a cellular telephone (and/or Public Switched TelephoneNetwork (PSTN)) network, and which comprise portions of the network 104.

While the network 104 is depicted in FIG. 1 as a single object, thenetwork 104 may comprise any number, type, and/or configuration ofnetworks that is or becomes known or practicable. According to someembodiments, the network 104 may comprise a conglomeration of differentsub-networks and/or network components interconnected, directly orindirectly, by the components 102 a-n, 106, 110, 140 of the system 100.The network 104 may comprise one or more cellular telephone networkswith communication links between the user devices 102 a-n and thecontroller device 110, for example, and/or may comprise a BLE, NFC, RF,and/or a “personal” network comprising short-range wirelesscommunications between the user device 102 and the smart card 106, forexample.

According to some embodiments, the smart card 106 may comprise anyquantity, type, and/or configuration of pre-stored data device and/orobject that is or becomes known or practicable. The smart card 106 maycomprise, for example, (i) a barcode card comprising a human and/orcomputer-readable indicia coupled (e.g., printed, etched, emblazoned)thereto, (ii) a magnetic stripe or “mag stripe” card comprising amagnetically-encoded data strip, (iii) an Integrated Circuit (IC)micro-processer card or “chip card” comprising a micro-processing unit(e.g., an eight-bit, sixteen-bit, or thirty-two-bit processor), someamount of Read-Only Memory (ROM), and/or some amount of Random AccessMemory (RAM), (iv) an IC memory card comprising larger data storagecapacity (e.g., ROM) than an IC micro-processor card, but not having aprocessing unit, and/or (v) an optical memory card comprising anoptically-encoded data portion (e.g., similar to the optical datastorage of a Digital Video Disk (DVD)). In some embodiments, the smartcard 106 may store information utilizing one or more storage devicesand/or mediums (not shown), such as, but not limited to, a solid-statememory device, a magnetic stripe, barcode (e.g., 2-D and/or 3-D barcode;e.g., a 2-D matrix barcode, such as a Quick Response (QR®) code)), RFIDtag, and/or other NFC information device that stores identifying and/ordescriptive information (e.g., account metadata). According to someembodiments, the smart card 106 may provide access to the stored datavia various transmission and/or interrogation methods, such as, but notlimited to, a bar code reader, magnetic stripe reader, NFC receiver (ortransceiver), Bluetooth® device, and/or other optical, magnetic,passive-inductive, and/or proximity device configured and/or coupled toread information from the smart card 106.

In some embodiments, the smart card 106 may comprise and/or the system100 may comprise a third-party device (not separately shown) that mayitself comprise any type or configuration of a computerized processingdevice, such as a PC, laptop computer, computer server, database system,and/or other electronic device, devices, or any combination thereof. Insome embodiments, such a third-party device may be owned and/or operatedby a third-party (i.e., an entity different than any entity owningand/or operating either the user devices 102 a-n or the controllerdevice 110; such as a data provider entity). The third-party device may,for example, be owned and/or operated by a data and/or data serviceprovider, such as Dun & Bradstreet® Credibility Corporation of ShortHills, N.J. (and/or a subsidiary thereof, such as Hoovers™), Deloitte®Development, LLC of London, GB, Experian™ Information Solutions, Inc. ofCosta Mesa, Calif., EagleView® Technologies, Inc. of Bellevue, Wash.,and/or Edmunds.com®, Inc. of Santa Monica, Calif. In some embodiments,the third-party device may supply and/or provide data, such as aerialimagery, Global Positioning System (GPS) data, vehicle diagnostic data,traffic camera images, blueprints, maps, etc., to the controller device110 and/or the user devices 102 a-n. In some embodiments, thethird-party device may comprise a plurality of devices and/or may beassociated with a plurality of third-party entities and/or may beseparate from the smart card 106.

In some embodiments, the controller device 110 may comprise anelectronic and/or computerized controller device, such as a computerserver communicatively coupled to interface with the user devices 102a-n and/or the smart card 106 (directly and/or indirectly). Thecontroller device 110 may, for example, comprise one or more PowerEdge™R830 rack servers manufactured by Dell®, Inc. of Round Rock, Tex., whichmay include one or more Twelve-Core Intel® Xeon® E5-4640 v4 electronicprocessing devices. In some embodiments, the controller device 110 maycomprise a plurality of processing devices specially programmed toexecute and/or conduct processes that are not practicable without theaid of the controller device 110. The controller device 110 may, forexample, execute a plurality of AI logic modules that work in concert toprocess multivariate inputs to calculate and/or compute a result basedon a user query/submission, as described herein, such automaticmulti-step AI rules-based analysis not being capable of being conductedwithout the benefit of the specially-programmed controller 110,particularly not within timeframes that prevent excessive queuing and/ordelays (e.g., within a matter of hours). According to some embodiments,the controller device 110 may be located remotely from one or more ofthe user devices 102 a-n and/or the smart card 106. The controllerdevice 110 may also or alternatively comprise a plurality of electronicprocessing devices located at one or more various sites and/orlocations.

According to some embodiments, the controller device 110 may storeand/or execute specially programmed instructions to operate inaccordance with embodiments described herein. The controller device 110may, for example, execute one or more AI, neural network, and/or otherprograms, modules, and/or routines that facilitate the provision ofmultivariate query results, e.g., in an online environment, as utilizedin various applications, such as, but not limited to, underwritingproduct claims processes, as described herein. According to someembodiments, the controller device 110 may comprise a computerizedprocessing device, such as a computer server and/or other electronicdevice to manage and/or facilitate queries and/or communicationsregarding the user devices 102 a-n. An insurance company employee,agent, claim handler, underwriter, and/or other user (e.g., customer,contractor, client, or company) may, for example, utilize the controllerdevice 110 to (i) identify customer account metadata (e.g., based ondata received from the smart card 106 and/or one or more of the userdevices 102 a-n), (ii) identify natural language data (e.g., based ondata received from one or more of the user devices 102 a-n), (iii)identify sensor data (e.g., based on data received from the smart card106, a third-party device, and/or one or more of the user devices 102a-n), (iv) generate a numeric expression for each of the identified datainputs (e.g., utilizing stored numeric conversion rules), (v) identifyone or more relevant historical cases based on an analysis and/orcomparison of numeric expressions representing historic data and thegenerated numeric expressions, (vi) calculate and/or compute a resultbased on the one or more relevant cases (e.g., an underwriting productclaim handling result), and/or (vii) output the result via an interfaceon the user's mobile device, as described herein.

In some embodiments, the controller device 110 and/or the smart card 106(and/or the user devices 102 a-n) may be in communication with thedatabase 140. The database 140 may store, for example, accountidentification data, preference and/or characteristics data, historicquery result data (e.g., claim handling result data), geo-location data,and/or classification data obtained from the user devices 102 a-n, thesmart card 106, historic query result metrics (e.g., statistics) definedby the controller device 110, data defining natural language analysisrules, metadata analysis rules, sensor data analysis rules, and/or queryprocessing rules (e.g., claim handling rules), and/or instructions thatcause various devices (e.g., the controller device 110, the smart card106, and/or the user devices 102 a-n) to operate in accordance withembodiments described herein. The database 140 may store, for example,one or more batch job files, data transformation scripts, metadataanalysis scripts, natural language analysis scripts, sensor dataanalysis scripts, account identification data, insured object data(e.g., type, capability, and/or location), and/or decision-making data(e.g., thresholds and/or logic). In some embodiments, the database 140may comprise any type, configuration, and/or quantity of data storagedevices that are or become known or practicable. The database 140 may,for example, comprise an array of optical and/or solid-state hard drivesconfigured to store policy and/or location data provided by (and/orrequested by) the user devices 102 a-n and/or smart card 106, metadataanalysis data (e.g., smart card identification and/or communication dataand/or metadata parsing and/or numeric conversion data), naturallanguage analysis data (e.g., speech-to-text analysis data, keywordsand/or phrases), sensor analysis data (e.g., analysis formulas and/ormathematical models descriptive of sensor data parsing, objectrecognition, object classification, and/or numeric conversion data),and/or various operating instructions, drivers, etc. While the database140 is depicted as a stand-alone component of the system 100 in FIG. 1,the database 140 may comprise multiple components. In some embodiments,a multi-component database 140 may be distributed across various devicesand/or may comprise remotely dispersed components. Any or all of theuser devices 102 a-n or the smart card 106 may comprise the database 140or a portion thereof, for example, and/or the controller device 110 maycomprise the database or a portion thereof.

Turning to FIG. 2, a mixed block and perspective diagram of a system 200according to some embodiments, is shown. In some embodiments, the system200 may comprise a mobile electronic device 202 in communication (e.g.,via one or more networks 204 a-b) with a smart card 206. In someembodiments, the smart card 206 may comprise a stored data indicia 208,e.g., in the form of a two-dimensional or matrix barcode, as depicted inFIG. 2. In some embodiments, the mobile electronic device 202 mayutilize a first network 204 a, such as a short-range wireless network,to communicate with the smart card 206. In some embodiments, the mobileelectronic device 202 may communicate via a second network 204 b, suchas a cellular communications network and/or the Internet, with a server210.

According to some embodiments, the mobile electronic device 202 maycomprise one or more communication antennas 214 a-b (e.g., a firstantenna 214 a, such as a Wi-Fi®, Bluetooth®, and/or other short-rangecommunications antenna, and/or a second antenna 214 b, such as such as acellular network or long-range antenna). The first antenna 214 a may beutilized for communications via the first network 204 a, for example,and/or the second antenna 214 b may be utilized for communications viathe second network 204 b. In some embodiments, the mobile electronicdevice 202 may comprise various input devices 216 a-b, such as a firstinput device or camera 216 a and/or a second input device and/ormicrophone 216 b. According to some embodiments, the mobile electronicdevice 202 may comprise one or more output devices 218 a-b (e.g., afirst output device 218 a, such as a display screen, and/or a secondoutput device 218 b, such as a speaker). In some embodiments, one ormore components, such as the display screen 218 a, may comprise both aninput device 216 a-b and an output device 218 a-b—e.g., the displayscreen 218 a may comprise a capacitive and/or touch-capable input/outputdevice. According to some embodiments, the mobile electronic device 202(and/or the display screen 218 a thereof) may output a Graphical UserInterface (GUI) 220 that provides output from and/or accepts input forthe mobile electronic device 202.

In some embodiments, the mobile electronic device 202 may comprise or bein communication with a first memory device 240 a and/or the server 210may comprise or be in communication with a second memory device 240 b.The first memory device 240 a may store, for example, a first or mobiledevice application 242 a and/or the second memory device 240 b may storea plurality of AI modules 242 b-1, 242 b-2, 242 b-3, 242 b-n and/orstored data 244. According to some embodiments, the GUI 220 may provideoutput from and/or accept input for the mobile device application 242 aexecuted by the mobile electronic device 202. In some embodiments, themobile electronic device 202 may conduct communications with the smartcard 206 and/or the server 210. The mobile electronic device 202 may,for example, execute the mobile device application 242 a to generate theinterface 220 and/or to activate the camera 216 a (or other sensor) tocapture data descriptive of and/or stored by the smart card 206. Asdepicted in FIG. 2, for example, the camera 216 a may be utilized tocapture an image of the smart card 206 and/or the stored data indicia208 thereof, and/or the first network 204 a may be utilized to transferdata (e.g., indicative of the stored data indicia 208) to the mobileelectronic device 202.

According to some embodiments, the mobile electronic device 202 (e.g.,via execution of the mobile device application 242 a) may process and/ortransmit any or all data received from (or indicative of, such as datafrom the camera 216 a) the smart card 206. Such data may be transmittedfrom the smart card 206 to the mobile electronic device 202, forexample, and the mobile device application 242 a executed by the mobileelectronic device 202 may implement stored rules and/or logic to analyzeand/or output the received data by transmitting the received and/orcaptured data to the server 210 (e.g., via the second network 204 b).According to some embodiments, data output via the first output device218 a (and/or the GUI 220) of the mobile electronic device 202 may bebased on and/or triggered by the data received from (and/or indicativeof) the smart card 206.

According to some embodiments, the server 210 may execute one or more ofthe AI modules 242 b-1, 242 b-2, 242 b-3, 242 b-n, e.g., in response toreceiving the data indicative of/from the smart card 206. According tosome embodiments, the server 210 and/or the second memory device 240 bmay store fewer or more modules, procedures, and/or programs than aredepicted in FIG. 2. In some embodiments, a first AI module 242 b-1 maycomprise and/or define programming logic that is directed to processing,analyzing, and/or converting metadata. The first AI module 242 b-1 may,for example, define instructions that accept the data indicative of/fromthe smart card 206 and/or query the stored data 244 as inputs andprovide an output comprising a numeric expression descriptive of theanalyzed/processed metadata. According to some embodiments, a second AImodule 242 b-2 may comprise and/or define programming logic that isdirected to processing, analyzing, and/or converting natural languagedata. The second AI module 242 b-2 may comprise, for example, a virtualassistant or “chat” module that accepts natural language (text and/orspeech) input form the mobile electronic device 202, processes the inputto identify natural language intents and/or responses, and/or providesan output comprising a numeric expression descriptive of theanalyzed/processed natural language data (e.g., inputs and/or inferredintents). In some embodiments, a third AI module 242 b-3 may compriseand/or define programming logic that is directed to processing,analyzing, and/or converting sensor data. The third AI module 242 b-3may comprise, for example, an image (and/or other sensor data type)analysis module that accepts image (and/or other sensor data) input formthe mobile electronic device 202, processes the input to identifyobjects described by the image data, and/or provides an outputcomprising a numeric expression descriptive of the analyzed/processedimage (and/or other sensor) data.

In some embodiments, the second memory device 240 b may store one ormore other AI modules 242 b-n. Such other AI modules 242 b-n may, forexample, comprise and/or define programming logic that is directed toidentifying and/or quantifying damage to one or more objects identifiedbased on data received from the mobile electronic device 202 (and/or thesmart card 206). Such a module may, for example, utilize data capturedby the camera 216 a (and/or other sensor of the mobile electronic device202) as input, analyze features within the imagery and/or data (e.g.,expected, designed, and/or typical information from the location at aprevious time, as compared to actual information from the location atthe current time) to identify one or more areas of damage ornon-conformity. In some embodiments, such damage information and/oridentification may be compared and/or cross-referenced with repairand/or replacement data (e.g., the stored data 244, or a portionthereof) to calculate an expected monetary amount of damage (e.g., loss)for the analyzed location and/or objects.

In some embodiments, the mobile electronic device 202 may comprise asmart mobile phone, such as the iPhone® 8 or a later generation iPhone®,running iOS 10 or a later generation of iOS, supporting LocationServices. The iPhone® and iOS are produced by Apple Inc., however,embodiments herein are not limited to any particular portable computingdevice or smart mobile phone. For example, the mobile electronic device202 may take the form of a laptop computer, a handheld computer, apalm-size computer, a pocket computer, a palmtop computer, a PersonalDigital Assistant (PDA), a tablet computer, an electronic organizer, amobile phone, a portable/mobile phone, a feature phone, a smartphone, atablet, a portable/mobile data terminal, an iPhone®, an iPad®, an iPod®,an Apple® Watch (or other “smart” watch), and other portable form-factordevices by any vendor containing at least one Central Processing Unit(CPU) and a wireless communication device (e.g., one or more of thecommunication antennas 214 a-b).

According to some embodiments, the mobile electronic device 202 runs(i.e., executes) the mobile device software application 242 a (“app”)that causes the generation and/or output of the GUI 220. In someembodiments, the app works with Location Services supported by an iOSoperating system executing on the mobile electronic device 202. The app242 a may include, comprise, and/or cause the generation of the GUI 220,which may be utilized, for example, for transmitting and/or exchangingdata with the smart card 206 and/or the server 210. In some embodiments,once the app 242 a receives and/or captures data from the smart card206, the app 242 a may translate the received data and/or utilize thedata to trigger and/or generate one or more images and/or other outputprovided to a user (not shown) of the mobile electronic device 202.

Fewer or more components 202, 204 a-b, 206, 208, 210, 214 a-b, 216 a-b,218 a-b, 220, 240 a-b, 242 a, 242 b-1, 242 b-2, 242 b-3, 242 b-n, 244and/or various configurations of the depicted components 202, 204 a-b,206, 208, 210, 214 a-b, 216 a-b, 218 a-b, 220, 240 a-b, 242 a, 242 b-1,242 b-2, 242 b-3, 242 b-n, 244 may be included in the system 200 withoutdeviating from the scope of embodiments described herein. In someembodiments, the components 202, 204 a-b, 206, 208, 210, 214 a-b, 216a-b, 218 a-b, 220, 240 a-b, 242 a, 242 b-1, 242 b-2, 242 b-3, 242 b-n,244 may be similar in configuration and/or functionality to similarlynamed and/or numbered components as described herein. In someembodiments, the system 200 (and/or portions thereof) may comprise amultivariate AI smart card program, system, and/or platform programmedand/or otherwise configured to execute, conduct, and/or facilitate themethods 400, 500, 600 of FIG. 4, FIG. 5, and/or FIG. 6 herein, and/orportions or combinations thereof.

Turning now to FIG. 3A and FIG. 3B, a diagram of a smart card 306 anddiagram of a barcode 308 (and/or other machine-readable indicia)according to some embodiments are shown, respectively. The smart card306 may, for example, comprise a body 306-1 that is coupled to and/orcomprises the barcode 308 that stores information (e.g., metadata and/oridentifying information) descriptive of one or more accounts, customers,insured objects, characteristics, settings, and/or other parameters. Asdepicted, the barcode 308 may comprise a machine-readable optical label,engraving, etching, and/or feature that is descriptive of storedinformation and that is disposed on a front surface 306-2 of the body306-1. In some embodiments, the body 306-1 may be constructed ofcard-stock, paper, metal, carbon-fiber, and/or plastic, e.g., asynthetic plastic polymer such as PolyVinyl Chloride (PVC) and/or athermoplastic polymer resin, such as PolyEthylene Terephthalate (PET) orPolyEthylene Terephthalate Glycol (PETG). The body 306-1 may, in someembodiments, be generally rectangular-shaped with rounded corners and/orhave dimensions approximating standard credit card-sizes, such aseighty-five and six tenths millimeters by fifty-three and ninety-eighthundredths millimeters (85.6 mm×53.98 mm; or 3.370″×2.125″) with athickness of seventy-six hundredths millimeters (0.76 mm; 0.03125″).

According to some embodiments, the smart card 306 may also oralternatively comprise an NFC device 318 and/or a memory chip 340. Thememory chip 340 may, for example, comprise a 256K (262,144 bits) or 512K(524,288 bits) serial Electrically Erasable and Programmable Read-OnlyMemory (EEPROM) chip embedded between layers (not shown) of thesubstrate utilized to construct the body 306-1 of the smart card 306. Insome embodiments, the memory chip 306-3 may store the same informationas the barcode 308 and/or may store additional information. According tosome embodiments, the data stored on the memory chip 340 may beaccessible via the NFC device 318. The NFC device 318 may, for example,comprise a micro NFC/RFID transponder, such as an NTAG® 213 IC thatoperates in accordance with the ISO 14443A “Identificationcards—Contactless integrated circuit cards—Proximity cards” standardpublished by the International Organization for Standardization (ISO) ofGeneva, Switzerland (2018), and available from NXP® SemiconductorsNetherlands B.V. of Eindhoven, Netherlands. The NFC device 318 may, insome embodiments, comprise a passively activated and/or poweredtransponder operable to provide stored information (e.g., stored by theNFC device 318 and/or the memory chip 340) wirelessly to an external andproximate device (not shown; e.g., the mobile electronic device 202 ofFIG. 2 herein).

In some embodiments, such as in the case that the barcode 308 isutilized, and referring specifically to FIG. 3B for greater detail, thebarcode 308 may store account data and/or metadata via a plurality ofencoded and/or arranged black squares 308-1 a in a square grid on awhite background 308 a-1 b. Such encoded data can be read by an imagingdevice (not shown) and processed using known Reed-Solomon errorcorrection techniques to decode the stored data. In operation, a matrixbarcode, such as the example barcode 308 shown in FIG. 3A may be imagedby a 2-dimensional imaging sensor and then digitally processed by anelectronic device. The processing may generally comprise anidentification and/or location of three positioning symbols (e.g.,embedded squares) 308-2 a, 308-2 b, 208-2 c disposed proximate to threecorners of the barcode 308 image, with image alignment and normalizationbeing confirmed utilizing an alignment symbol (e.g., a smaller embeddedsquare) 308-3 proximate to a fourth corner of the barcode 308 image. Insome embodiments, the black squares 308-1 a encoding the data may beconverted to binary numbers and validated with an error-correctingalgorithm (e.g., a Reed-Solomon algorithm) in accordance with theISO/IEC 18004:2015 “Information—Automatic identification and datacapture techniques—QR Code barcode symbology” specification published bythe ISO (2015).

According to some embodiments, the barcode 308 may comprise any typeand/or configuration of machine-readable indicia, such as a Version 40(177×177) QR® code storing up to one thousand eight hundred andfifty-two (1,852) characters of ASCII text and/or a Version 4 (33×33)QR® code as depicted in FIG. 3A, and capable of storing up to fifty (50)characters of data. In some embodiments, the barcode 308 may comprisespecial regions of data that are utilized to effect operational decodingof the stored data. The barcode 308 may comprise, for example, a “quietzone” 308-4 that provides an optical imaging buffer between the storeddata and an external visual elements, encoded version information areas308-5 that store decoding version information, and/or formatting areas308-6 that store data descriptive of the format of the stored data.According to some embodiments, the barcode 308 may also or alternativelycomprise a logo 308-7 or other artwork, images, text, and/or non-datafeatures to distinguish the barcode 308 from those utilized by otherentities. In the case of the logo 308-7 depicted in FIG. 3B, forexample, the design of the barcode 308 may be centered upon the logo308-7, while the layout and visual depiction of the stored data (e.g.,via the black squares 308-1 a) and decoding elements 308-2 a, 308-2 b,308-2 c, 308-3, 308-4, 308-5, 308-6 may change depending upon the typeof barcode 308 chosen for the smart card 306 and based on the specificdata stored thereon.

Fewer or more components 306-1, 306-2, 308-1 a, 308-1 b, 308-2 a, 308-2b, 308-2 c, 308-3, 308-4, 308-5, 308-6, 308-7, 318, 340 and/or variousconfigurations of the depicted components 306-1, 306-2, 308-1 a, 308-1b, 308-2 a, 308-2 b, 308-2 c, 308-3, 308-4, 308-5, 308-6, 308-7, 318,340 may be included in the smart card 306 without deviating from thescope of embodiments described herein. In some embodiments, thecomponents 306-1, 306-2, 308-1 a, 308-1 b, 308-2 a, 308-2 b, 308-2 c,308-3, 308-4, 308-5, 308-6, 308-7, 318, 340 may be similar inconfiguration and/or functionality to similarly named and/or numberedcomponents as described herein. In some embodiments, the smart card 306(and/or portions thereof) may comprise a multivariate AI smart cardprogram, system, and/or platform programmed and/or otherwise configuredto execute, conduct, and/or facilitate the methods 400, 500, 600 of FIG.4, FIG. 5, and/or FIG. 6 herein, and/or portions or combinationsthereof.

III. Multivariate AI Smart Card Methods

Referring to FIG. 4, a systemic flow diagram of a method or process 400according to some embodiments is shown. In some embodiments, the process400 may comprise and/or define a method for multivariate AI smart carddecision-making result generation (e.g., based on user/customerqueries/input). The process 400 may, for example, be executed by varioushardware and/or logical components via interactive communications, whichmay involve communications between any or all of a handheld device 402,a smart card 406, and/or a server 410. According to some embodiments,the handheld device 402 may comprise a first processing unit 412 aand/or the server 410 may comprise a second processing unit 412 b. Insome embodiments, the handheld device 402 may comprise and/or be incommunication with a first memory 440 a and/or the server 410 maycomprise and/or be in communication with a second memory 440 b. Thefirst memory 440 a of the handheld device 402 may store, for example,application instructions 442 a. According to some embodiments, thesecond memory 440 b of the server 410 may store an AI image module 442b-1, an AI text module 442 b-2, and/or an AI metadata module 442 b-3. Insome embodiments, the second memory 440 b and/or the server 410 maycomprise and/or store metadata 444 and/or the metadata 444 (or a portionthereof) may reside externally to the server 410 (e.g., on one or morethird-party devices; not shown).

In some embodiments, the process 400 (e.g., for multivariate AI smartcard decision-making result generation based on user/customerqueries/input) may comprise providing first input by the smart card 406to the handheld device 402 (and/or the first processing device 412 athereof), at “1”. The handheld device 402 may query or interrogate thesmart card 406 to retrieve the first input, for example, and/or mayutilize a sensor (not shown), such as a camera, to acquire the firstinput, e.g., as described herein. In some embodiments, the smart card406 may comprise NFC, RFID, and/or other short-range network or wirelesscommunications capability that is utilized to transmit and/or pass thefirst input (such as account identification information) to the handhelddevice 402. According to some embodiments, the first processing device412 a may pass the first input to and/or initialize the applicationinstructions 442 a, at “2”. According to some embodiments, theapplication instructions 442 a may identify a communication address ofthe server 410, e.g., based on the first input from the smart card 406.The transmitting at “3” may, in some embodiments, result from anautomatic activation of a hard-coded network address or remoteidentifier of the server 410 embedded within and/or accessible to theapplication instructions 442 a. In some embodiments, the applicationinstructions 442 a may pass the first input to, call, and/or query thesever 410 (and/or the second processing unit 412 b thereof), at “3”. Insome embodiments, the application instructions 442 a may edit and/orappend data to the first input (e.g., data descriptive of the handhelddevice 402 and/or user/customer query data, such as claim handling data)prior to transmitting to the server 410.

In some embodiments, the second processing unit 412 b may utilize thereceipt of the first input to trigger a call (e.g., a first AI call),query, and/or or initiation of the AI metadata module 442 b-3, at “4”.According to some embodiments, as shown by the dotted line from “4” inFIG. 4, the first input may also or alternatively be utilized toinitiate a query to the metadata 444. The metadata 444 may comprise, forexample, data stored in relation to data elements received as part ofthe first input, e.g., an account, object, electronic device, and/orlocation identifier. The metadata 444 may be queried utilizing suchrelations to identify a portion or subset of related metadata, at “5”.According to some embodiments, the identified portion/subset of metadataand/or the first input may be passed as first AI input to the AImetadata module 442 b-3, at “6”.

According to some embodiments, the AI metadata module 442 b-3 mayexecute stored AI instructions to process, analyze, and/or evaluate thesubset of the metadata and/or the first input, at “7”. The AI metadatamodule 442 b-3 may, for example, compare the first input and/or thesubset of the metadata to stored data (not separately shown) descriptiveof other user/customer queries, accounts, losses, claims, etc., e.g., toidentify similarities between previous queries and the currentuser/customer query. According to some embodiments, values for variousparameters of the first input and/or the subset of the metadata may becompared to other stored values for the same parameters (but fordifferent users/customers and/or events) and may be identified assimilar in the case that the values are within a predetermined thresholdvalue range of each other. In some embodiments, the first input and/orthe subset of the metadata may be processed utilizing stored rulesdefined and/or executed by the AI metadata module 442 b-3 to generate,define, calculate, and/or otherwise compute a first or metadata numericvalue representative of the first input/subset of the metadata, e.g., afirst AI output “I”. According to some embodiments, the first AI output“I” may also or alternatively comprise multi-factor authentication data,such as a security question and/or code that may be based on the firstinput and/or the subset of the metadata. In some embodiments, the firstAI output “I” may also or alternatively comprise the subset of themetadata or a portion thereof.

In some embodiments, the AI metadata module 442 b-3 may pass or transmitthe first AI output “I” to the second processing unit 412 b, at “8”. Thetransmission at “8” may, for example, comprise a response (“RS”) to theprovision of the first input at “4”. According to some embodiments, thesecond processing unit 412 b may utilize the first AI output “I” toidentify and/or generate a query, such as a multi-factor authenticationchallenge question, based on the subset of the metadata, etc., at “9”.In some embodiments, the server 410 (and/or the second processing unit412 b thereof) may transmit the query/challenge to the handheld device402 (and/or the first processing unit 412 a thereof), at “10”. In thecase that the server 410 is in communication with the handheld device402 via the Internet and/or a cellular telephone network, for example,the server 410 may generate a packet-based communication signal thatconveys the generated query/challenge to the handheld device 402.

In some embodiments, the first processing unit 412 a of the handhelddevice 402 may utilize the first AI output “I” to trigger and/oractivate the application instructions 442 a, at “11”. The applicationinstructions 442 a may, for example, output (e.g., via a GUI; notseparately shown) an indication of the first AI output “I”, such as amulti-factor authentication challenge, security question, policyquestion, etc., and/or otherwise query the user, at “12”. According tosome embodiments, the user may provide input to the handheld device 402,e.g., in response to the outputting of the first AI output “I”, and theapplication instructions 442 a may transmit or forward such second inputto the server 410 (and/or the second processing device 412 b thereof),at “13”. In some embodiments, such as in the case that the applicationinstructions 442 a output a query to the user, the second input maycomprise a natural language response to the query. The natural languageresponse may, for example, comprise text, audio, video, and/or othernatural language and/or communicative information conveyed by the user.

In some embodiments, the second processing unit 412 b of the server 410may utilize the receipt of the second input to trigger a call (e.g., asecond AI call), query, and/or or initiation of the AI text module 442b-2, at “14”. According to some embodiments, the second input may bepassed as second AI input to the AI text module 442 b-2. In someembodiments, the AI text module 442 b-2 may execute stored AIinstructions to process, analyze, and/or evaluate the second input, at“15”. The AI text module 442 b-2 may, for example, compare the secondinput (e.g., a natural language response) to stored data (not separatelyshown) descriptive of natural language intent mappings to identifysimilarities between prestored intent mappings and the currentuser/customer input/response. In some embodiments, the compared and/oridentified mappings may be utilized to formulate one or more naturallanguage responses or queries to be sent to the user, defining anautomated and interactive AI chat session with the user.

According to some embodiments, values for various parameters of thesecond input (e.g., ASCII identifiers, word identifiers, phraseidentifiers) may be compared to other stored values for the sameparameters (but for different users/customers and/or events) and may beidentified as similar in the case that the values are within apredetermined threshold value range of each other. In some embodiments,the second input may be processed utilizing stored rules defined and/orexecuted by the AI text module 442 b-2 to generate, define, calculate,and/or otherwise compute a second or natural language numeric valuerepresentative of the second input, e.g., a second AI output “II”.According to some embodiments, the second AI output “II” may also oralternatively comprise one or more natural language responses formulatedby the AI text module 442 b-2.

In some embodiments, the AI text module 442 b-2 may pass or transmit thesecond AI output “II” to the second processing unit 412 b, at “16”. Thetransmission at “16” may, for example, comprise a response (“RS”) to theprovision of the second input at “14”. According to some embodiments,and as identified by the dotted feedback loop line in FIG. 4, the secondprocessing unit 412 b may utilize the second AI output “II” to identifyand/or generate a query that is passed on to the handheld device 402(and/or the application instructions 442 a thereof) and the user mayprovide additional natural language input that is sent back to theserver 410 at “13”, e.g., in furtherance of the automated andinteractive AI chat session with the user.

According to some embodiments, the application instructions 442 a mayoutput (e.g., via the GUI) an indication of the second AI output “II”,such as a request to provide and/or capture an image (and/or othersensor data of the handheld device 402) and/or otherwise query the user,at “17”. According to some embodiments, the user may provide input tothe handheld device 402, e.g., in response to the outputting of thesecond AI output “II”, and the application instructions 442 a maytransmit or forward such third input to the server 410 (and/or thesecond processing device 412 b thereof), at “18”. In some embodiments,such as in the case that the application instructions 442 a output animage request and/or sensor data query to the user, the third input maycomprise one or more images, audio, video, and/or other sensor datareadings and/or depictions.

In some embodiments, the second processing unit 412 b of the server 410may utilize the receipt of the third input to trigger a call (e.g., athird AI call), query, and/or or initiation of the AI image module 442b-1, at “19”. According to some embodiments, the third input may bepassed as third AI input to the AI image module 442 b-1. In someembodiments, the AI image module 442 b-1 may execute stored AIinstructions to process, analyze, and/or evaluate the third input, at“20”. The AI image module 442 b-1 may, for example, compare the thirdinput (e.g., image and/or other sensor data) to stored data (notseparately shown) descriptive of object images and/or characteristics(e.g., visual and/or other sensor characteristics) to identifysimilarities between prestored object data and the captured image data.In some embodiments, the comparison may be utilized to identify objectsand/or other features within and/or described by the third input and/orcompare such objects and/or features to objects and/or features forother accounts, customers, and/or events.

According to some embodiments, values for various parameters of thethird input (e.g., identified object types, sizes, temperature, color,conditions, and/or other characteristics) may be compared to otherstored values for the same parameters (but for different users/customersand/or events) and may be identified as similar in the case that thevalues are within a predetermined threshold value range of each other.In some embodiments, the third input may be processed utilizing storedrules defined and/or executed by the AI image module 442 b-1 togenerate, define, calculate, and/or otherwise compute a third or imagenumeric value representative of the third input, e.g., a third AI output“III”.

In some embodiments, the AI image module 442 b-1 may pass or transmitthe third AI output “III” to the second processing unit 412 b, at “21”.The transmission at “21” may, for example, comprise a response (“RS”) tothe provision of the third input at “19”. According to some embodiments,the second processing unit 412 b may utilize the first AI output “I”,the second AI output “II”, and/or the third AI output “III” to generate,calculate, and/or otherwise compute a query result (e.g., a claimhandling determination or result in the case of a claim of loss query),at “22”. The second processing unit 412 b may, for example, execute aclaim handling module (not shown) that defines automated AI-based claimhandling instructions. According to some embodiments, the instructionsmay utilize the first, second, and/or third numeric values generate bythe AI modules 442 b-1, 442 b-2, 442 b-3 to identify one or morenumerically similar previous claim handling (or other query)determinations and calculate and/or compute the current result based onthe previous result(s) from any identified similar matters. In someembodiments, the similarity of previous matters may be identified basedupon a mathematical comparison of each of the first, second, and thirdnumeric values to values stored in relation to corresponding variablesand/or parameters. In some embodiments, the three values may bemathematically combined (e.g., concatenated, aggregated, summed,averaged, etc.) to define a single numeric expression that is comparedto corresponding single numeric expressions for each of a plurality ofprevious matters. In the case that the mathematical difference (simple,absolute, and/or based on one or more formulas and/or models) betweenthe current single numeric expression and a previously recorded matter'snumeric expression is within a predefined threshold, similarity may bedefined.

According to some embodiments, the server 410 (and/or the secondprocessing unit 412 b thereof) may transmit the query/claim result tothe handheld device 402 (and/or the first processing unit 412 athereof), at “23”. In the case that the server 410 is in communicationwith the handheld device 402 via the Internet and/or a cellulartelephone network, for example, the server 410 may generate apacket-based communication signal that conveys the generated result tothe handheld device 402. In some embodiments, the first processing unit412 a of the handheld device 402 may utilize the receipt of the resultto trigger and/or activate the application instructions 442 a, at “24”.The application instructions 442 a may, for example, output (e.g., viathe GUI) an indication of the query/claim result, at “25”. In such amanner, for example, the user may utilize the smart card 406 to quicklyand easily provide error-free information to the server 410, e.g., incombination with user inputs (e.g., natural language and/or image/sensordata) to obtain an automated query/claim result.

Turning now to FIG. 5, a flow diagram of a method 500 according to someembodiments is shown. In some embodiments, the method 500 may beperformed and/or implemented by and/or otherwise associated with one ormore specialized and/or specially-programmed computers (e.g., one ormore of the user devices 102 a-n, mobile electronic device 202, handhelddevice 402, the controller device 110, the servers 210, 410, the smartcards 106, 206 306, 406, and/or the apparatus 810 of FIG. 1, FIG. 2,FIG. 3, FIG. 4, and/or FIG. 8 herein), computer terminals, computerservers, computer systems and/or networks, and/or any combinationsthereof (e.g., by one or more multi-threaded and/or multi-coreprocessing units of a multivariate AI smart card data processingsystem). In some embodiments, the method 500 may be embodied in,facilitated by, and/or otherwise associated with various inputmechanisms and/or interfaces (such as the interfaces 220, 720 a-d, 820of FIG. 2, FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, and/or FIG. 8 herein).

The process diagrams and flow diagrams described herein do notnecessarily imply a fixed order to any depicted actions, steps, and/orprocedures, and embodiments may generally be performed in any order thatis practicable unless otherwise and specifically noted. While the orderof actions, steps, and/or procedures described herein is generally notfixed, in some embodiments, actions, steps, and/or procedures may bespecifically performed in the order listed, depicted, and/or describedand/or may be performed in response to any previously listed, depicted,and/or described action, step, and/or procedure. Any of the processesand methods described herein may be performed and/or facilitated byhardware, software (including microcode), firmware, or any combinationthereof. For example, a storage medium (e.g., a hard disk, Random AccessMemory (RAM) device, cache memory device, Universal Serial Bus (USB)mass storage device, and/or Digital Video Disk (DVD); e.g., thememory/data storage devices 140, 240 a-b, 340, 440 a-b, 840, 940 a-e ofFIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 8, FIG. 9A, FIG. 9B, FIG. 9C, FIG.9D, and/or FIG. 9E herein) may store thereon instructions that whenexecuted by a machine (such as a computerized processor) result inperformance according to any one or more of the embodiments describedherein.

In some embodiments, the method 500 may comprise receiving (e.g., by anelectronic processing device, from a mobile application of a mobileelectronic device of an insured party/user, as an initiation of aprocess to submit a current insurance claim, and/or via a wirelesselectronic network) data stored on a smart card from a mobile (e.g.,portable) electronic device (e.g., via an electronic communicationsnetwork and/or by an electronic processing device), at 502. According tosome embodiments, the smart card data may be obtained from an indicationof computer-readable data stored on the smart card. The smart card maycomprise a barcode (e.g., a matrix barcode) that is scanned and/orcaptured or otherwise read by the mobile electronic device, for example,with the content of the barcode being descriptive and/or indicative ofthe stored data. According to some embodiments, the mobile electronicdevice may forward, provide, and/or transmit the stored data (e.g., anindication thereof) to a server of an AI system, as described herein. Insome embodiments, the smart card data may be obtained from a memorydevice of the smart card such as via an NFC/RFID device in communicationwith the smart card memory device. The mobile electronic device may, forexample, interrogate the smart card via one or more NFC/RFID signalsand/or fields to obtain the stored data. According to some embodiments,the smart card data may comprise data descriptive of and/or identifyingan account (e.g., an insurance and/or underwriting account), a customeror other user, an address of the user, an insured object (e.g., anobject identifier such as a Vehicle Identification Number (VIN)), anenvironmental condition (e.g., temperature), time, location, query/claim(e.g., description and/or identifier), etc. In some embodiments, thesmart card data may comprise and/or be considered metadata descriptiveof and/or related to a query/claim of the user of the mobile electronicdevice. According to some embodiments, the smart card may provide and/ortransmit the stored data directly to the server without requiring themobile electronic device.

In some embodiments, the method 500 may comprise conducting (e.g., bythe electronic processing device) a two-factor authentication challenge,at 504. In response to the receiving of the smart card data, forexample, the electronic processing device may query a database or otherdata store to identify a particular two-factor (or multi-factor)authentication challenge (and/or other data, such as metadata).Utilizing the data stored on the smart card (and/or data relatedthereto), for example, a multi-factor communication address and/orchallenge, such as a security question (e.g., account metadata), may beretrieved and/or generated. A communication address of the user, such asan e-mail address, phone number, etc., may, in some embodiments, belooked up. According to some embodiments, the address may comprise anaddress different from and/or dissociated with the mobile electronicdevice. In some embodiments, such as in response to the identifying ofthe two-factor authentication challenge, the two-factor authenticationchallenge may be transmitted to the identified communication address ofthe user (e.g., to the mobile electronic device and/or to a differentdevice associated with the address). According to some embodiments, anindication of a response and/or answer to the two-factor authenticationchallenge may be received by the electronic processing device (e.g.,from the mobile electronic device and/or from a different deviceassociated with the address). The answer may comprise, for example, acode, a voice imprint, a facial image, a finger/thumbprint impression,etc. In some embodiments, such as in response to the receiving of theanswer to the challenge, the electronic processing device mayauthenticate the answer. The answer may be compared, for example, topre-stored and/or specially generated data (e.g., metadata and/or aspecially generated passcode) to identify a match. In the case that theanswer matches the pre-stored/generated data, the identity and/orauthorization of the user may be inferred. In the case that the answerdoes not match, an additional challenge may be transmitted and/orcommunications with the user may be terminated.

According to some embodiments, the method 500 may comprise conducting(e.g., by the electronic processing device) AI metadata processing, at506. Metadata stored in relation to the smart card data and/orcomprising the smart card data (or a portion thereof) may, for example,be processed in accordance with pre-stored AI instructions such as maydefine a claim similarity AI model (or a first portion thereof, such asa first AI module), e.g., stored by a non-transitory computer-readablememory device in communication with the electronic processing device. Insome embodiments, the method 500 and/or the AI metadata processing at506 may comprise retrieving the metadata, at 508. The metadata may bestored in relation to the smart card data or a portion thereof, forexample, and may be retrieved by querying a database storing themetadata by utilizing the smart card data (or a portion thereof) asquery input. According to some embodiments, the method 500 and/or the AImetadata processing at 506 may comprise generating a metadata numeric,at 510. Either or both of the smart card data and any retrieved metadatarelated thereto may, for example, be processed via the first AI moduleto define a first or metadata numeric expression. In some embodiments,the execution of the claim similarity AI model by the electronicprocessing device may convert the data stored on the smart card and/orthe related metadata into a first number or first set of numbers. Thefirst AI module may, for example, apply a first mathematical model tothe smart card/metadata that utilizes the smart card/metadata as inputand generates (e.g., in response to receiving the input) one or morenumeric expressions, such as a single number, a series of numbers,and/or a matrix of numbers.

In some embodiments, the method 500 may comprise conducting (e.g., bythe electronic processing device) AI sensor data processing, at 512.Sensor data stored on and/or obtained by the mobile electronic device(and/or the smart card) may, for example, be processed in accordancewith pre-stored AI instructions such as may define the claim similarityAI model (or a second portion thereof, such as a second AI module)and/or a claim severity AI model (e.g., a third AI module), e.g., storedby the non-transitory computer-readable memory device in communicationwith the electronic processing device. According to some embodiments,the method 500 and/or the AI sensor data processing at 512 may comprisereceiving the sensor data from the portable electronic device, at 514.An indication of sensor data obtained by the mobile electronic devicemay, for example, be sent by the mobile electronic device to the serverin response to a request for the data. As described herein, for example,the electronic processing device may request that the user provide thesensor data to supplement the initial query/claim and/or descriptionthereof. In some embodiments, the sensor data may comprise one or moreimages, IR readings, location readings, temperature readings,accelerometer readings, light readings, sound readings and/orrecordings, etc. The mobile electronic device (and/or the smart card)may comprise, for example, a camera, home automation and/or securitysystem, and/or other sensor that is triggered and/or utilized to capturethe sensor data. In some embodiments, the method 500 and/or the AIsensor data processing at 512 may comprise generating a sensor datanumeric, at 516. Any or all sensor data may, for example, be processedvia the first AI module to define a second or sensor data numericexpression. In some embodiments, the execution of the claim similarityAI model (and/or the claim severity AI model) by the electronicprocessing device may convert the sensor data into a second number orsecond set of numbers. The second and/or third AI modules may, forexample, apply a second and/or third mathematical model to the sensordata that utilizes the sensor data as input and generates (e.g., inresponse to receiving the input) one or more numeric expressions, suchas a single number, a series of numbers, and/or a matrix of numbers. Insome embodiments, the second and/or third AI modules may comprise one ormore image and/or data analysis modules comprising programming logicthat searches the sensor data/imagery for shapes, colors, patterns,and/or other features or objects. According to some embodiments, thesecond and/or third AI modules may utilize one or more shape and/orpattern identification algorithms to identify areas within the sensordata/imagery that match stored shapes/patterns indicative of variousarchitectural, structural, natural, and/or other objects. Onceidentified, such objects may, in some embodiments, be utilized to definethe second numeric expression.

According to some embodiments, the method 500 may comprise conducting(e.g., by the electronic processing device) AI language data processing,at 518. Language data stored on and/or obtained by the mobile electronicdevice (and/or the smart card) may, for example, be processed inaccordance with pre-stored AI instructions such as may define the claimsimilarity AI model (or a third portion thereof, such as a fourth AImodule) and/or the claim severity AI model (e.g., the third AI module),e.g., stored by the non-transitory computer-readable memory device incommunication with the electronic processing device. In someembodiments, the method 500 and/or the AI language data processing at518 may comprise conducting a chat via the portable electronic device,at 520. An indication of language data obtained by the mobile electronicdevice may, for example, be sent by the mobile electronic device to theserver in response to a request for the data (e.g., a chatinitialization). As described herein, for example, the electronicprocessing device may send an introductory chat message to the user toinitiate an automated AI chat session. An execution of the AI chatmodule by the electronic processing device utilizing the data stored onthe smart card (and/or related metadata) may, for example, cause ageneration of at least one chat query. According to some embodiments, amobile application (e.g., a chat application) of the mobile electronicdevice may provide an indication of a chat response from the user, e.g.,in response to the generated at least one chat query. In someembodiments, the response may comprise language data (e.g., naturallanguage data), such as one or more words, sentences, icons (e.g.,emoji), audio, video, etc. According to some embodiments, the method 500and/or the AI language data processing at 518 may comprise generating alanguage data numeric, at 522. Any or all language data may, forexample, be processed via the third and/or fourth AI modules to define athird or language data numeric expression. In some embodiments, theexecution of the claim similarity AI model (and/or the claim severity AImodel) by the electronic processing device may convert the language datainto a third number or third set of numbers. The third and/or fourth AImodules may, for example, apply a third and/or fourth mathematical modelto the language data that utilizes the language data as input andgenerates (e.g., in response to receiving the input) one or more numericexpressions, such as a single number, a series of numbers, and/or amatrix of numbers.

In some embodiments, the method 500 may comprise identifying (e.g., bythe electronic processing device) one or more similar queries and/orclaims, at 524. The server of the AI system may, for example, conductone or more comparisons between data (e.g., metadata, sensor data,and/or language data) descriptive of the user's query/claim and datastored with respect to previous queries/claims. According to someembodiments, first, second, and/or third numbers (and/or sets ofnumbers) generated from and/or descriptive of the smart card/metadata,sensor data, and/or natural language data may be compared to numbersstored with respect to smart card/metadata, sensor data, and/or naturallanguage data of previous queries/claims. In some embodiments, the threenumbers/sets of numbers may be combined (e.g., mathematically) to form asingle combined number or set of numbers representative of the currentquery/claim, and this single number/set of numbers may be compared tosingle numbers/sets of numbers stored with respect to previousqueries/claims. According to some embodiments, different numbers and/ordigits representing different aspects of the various data elements maybe weighted by the AI models when generating the numbers and/or duringthe comparison process. In such a manner, for example, different aspectsof the smart card/metadata, sensor data, and/or natural language datamay be given higher priority when identifying similarities with previousqueries/claims. In some embodiments, one or more previous queries/claimsmay be identified as similar based upon a mathematical comparison of thenumbers. In the case that one or more compared numbers are within apredetermined mathematical confidence threshold, for example, anidentification of a similarity may occur.

According to some embodiments, the method 500 may comprise computing(e.g., by the electronic processing device) a query/claim result, at526. In some embodiments, the result may be based upon one or moreresults from at least one of the previous queries/claims identified asbeing similar to the current query/claim. An execution of a claimpricing AI model (e.g., a fifth AI module) by the electronic processingdevice and utilizing claim payment data stored with respect to the atleast one previous insurance claim may, for example, define a resolutionfor the current query and/or insurance claim. According to someembodiments, the result may be based upon a known result from the atleast one previous similar query/claim and based on a degree ofsimilarity between the current query/claim and the at least one previoussimilar query/claim. In the case that the similarity meets a firstthreshold of similarity (e.g., a first statistical and/or confidencethreshold) the result may comprise a first mathematical variant (e.g.,eighty percent (80%)) of the previous result, for example, while in thecase that the similarity meets a second threshold of similarity (e.g., asecond statistical and/or confidence threshold) the result may comprisea second mathematical variant (e.g., one hundred percent (100%)) of theprevious result.

In some embodiments, the method 500 may also or alternatively comprisecomputing (e.g., by the electronic processing device) a cause of loss(e.g., in the case that the user query comprises a submission of aninsurance claim) and/or a likelihood of the query/claim beingfraudulent. The AI system may comprise a cause of loss AI model (e.g., asixth AI module), for example, that conducts a comparing (e.g., upon anexecution by the electronic processing device), utilizing at least oneof the first, second, and third numbers, and by accessing the databasestoring data descriptive of the plurality of previous/historicquery/claims data, of the current insurance claim to previous insuranceclaims in which a cause of loss was identified. In some embodiments, thecause of loss AI model may identify (e.g., based on the comparison) oneor more similar previous claims and the causes of loss identified forsuch claim(s). According to some embodiments, the cause of loss AI modelmay conduct a computing, based on the comparing of the current insuranceclaim to previous insurance claims in which a cause of loss wasidentified, of a cause of loss for the current insurance claim. In someembodiments, the cause of loss may be utilized in the computing of theclaim result (e.g., at 526) and/or may be utilized to providecause-specific data and/or guidance to the user (e.g., tips forpreventing such type of loss in the future).

According to some embodiments, the AI system may comprise an AI frauddetection model (e.g., a seventh AI module) that conducts a comparing(e.g., upon an execution by the electronic processing device), utilizingat least one of the first, second, and third numbers, and by accessingthe database storing data descriptive of the plurality ofprevious/historic query/claims data, of the current query/claim toprevious queries/claims in which fraud had occurred. In someembodiments, the AI fraud detection model may identify (e.g., based onthe comparison) one or more similar previous queries/claims in whichfraud was previously determined to have occurred. According to someembodiments, the AI fraud detection model may conduct a computing, basedon the comparing of the current query/claim to the previousquery/claim(s) in which fraud had occurred, of a statistical likelihoodthat the current query/claim is fraudulent. A degree of similarity(e.g., based on one or more of the computed numeric expressions) betweenone or more aspects of the current query/claim and the previousfraudulent query/claim may be utilized, in some embodiments, to deriveand/or calculate the statistical likelihood, e.g., utilizing amathematical model defined by the AI fraud detection model.

In some embodiments, the method 500 may comprise transmitting the claimresult (e.g., via the wireless electronic network and/or to the mobileelectronic device), at 528. The AI system may, for example, send asignal indicative of the result to the mobile electronic device thatcauses the mobile device application to output an indication of theresult to the user. A GUI output by the mobile electronic device may, insome embodiments, provide a visual indication of the resolution for thecurrent query and/or insurance claim. In such a manner, for example, theuser may quickly and easily initiate a query/claim utilizing the smartcard (e.g., increasing the likelihood of accurate information beingsubmitted), conduct an online chat with an AI-based virtual assistant,upload supporting data (e.g., the sensor data), and receive a visualindication of the result of the submission, all in a timely and lesserror prone manner than in previous systems. According to someembodiments, the utilization of the multiple (e.g., three (3)) AImodules to process the various inputs and combine them into a singlenumber and/or set of numbers may greatly enhance the accuracy and speedof the results as compared to previous systems.

Referring now to FIG. 6, a systemic flow diagram of a method 600according to some embodiments is shown. In some embodiments, the method600 may be performed and/or implemented by and/or otherwise associatedwith one or more specialized and/or specially-programmed computers(e.g., one or more of the user devices 102 a-n, mobile electronic device202, handheld device 402, the controller device 110, the servers 210,410, the smart cards 106, 206 306, 406, and/or the apparatus 810 of FIG.1, FIG. 2, FIG. 3, FIG. 4, and/or FIG. 8 herein), computer terminals,computer servers, computer systems and/or networks, and/or anycombinations thereof (e.g., by one or more multi-threaded and/ormulti-core processing units of a multivariate AI smart card dataprocessing system). In some embodiments, the method 600 may be embodiedin, facilitated by, and/or otherwise associated with various inputmechanisms and/or interfaces (such as the interfaces 220, 720 a-d, 820of FIG. 2, FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, and/or FIG. 8 herein).

In some embodiments, the method 600 may comprise acquiring raw inputs,at “A”. Any or all of smart card data 644-1, language (e.g., naturallanguage) data 644-2, and/or sensor data 644-3 may, for example, beprovided as inputs into the method 600. According to some embodiments,the method 600 may comprise utilization of a data retrieval tool ormodule 642-1 to identify and/or retrieve metadata 644-4 from a database640. The data retrieval tool 642-1 may, for example, utilize the smartcard data 644-1 as a query input to identify and/or retrieve themetadata 644-4, e.g., stored in relation thereto. In some embodiments,the smart card data 644-1 and/or the metadata 644-4 may compriseindications of an account number, an address, a geo-location value(e.g., coordinates), a date, time, etc. According to some embodiments,the language data 644-2 may comprise text, audio, video, and/or otherrepresentations of natural human expression and/or language, such as atextual description of a query/claim of loss. In some embodiments, thesensor data 644-3 may comprise various sensor readings and/or data, suchas an image of the damage/loss and/or of a particular object.

According to some embodiments, the method 600 may comprise conductinginitial AI processing, at “B”. The initial AI processing may comprise,for example, utilization of a metadata AI processing tool or module642-2, a language data AI processing tool or module 642-3, and/or asensor data AI processing tool or module 642-4. The AI modules 642-2,642-3, 642-4 may, in some embodiments, apply one or more mathematicaland/or logical models to convert the smart card data 644-1, the languagedata 644-2, the sensor data 644-3, and/or the metadata 644-4 into one ormore numeric expressions that are utilized as AI inputs, at “C”. Thesmart card data 644-1 and/or the metadata 644-4 may, for example, beconverted (e.g., by the metadata AI processing module 642-2) into aseries of numbers, such as “[0.018426, 0.000433, . . . , −0.003075,−0.024303]” defining a metadata numeric 644-5. In some embodiments, themetadata numeric 644-5 may comprise five hundred and twelve (512)numbers arranged in a series, matrix, etc. According to someembodiments, the language data 644-2 may be converted (e.g., by thelanguage data AI processing module 642-3) into a series of numbers, suchas “[0.08311458, 0.01241801, . . . , −0.13994503, 0.00641239]” defininga language data numeric 644-6. In some embodiments, the language datanumeric 644-6 may comprise one thousand and twenty-four (1024) numbersarranged in a series, matrix, etc. In some embodiments, the sensor data644-3 may be converted (e.g., by the sensor data AI processing module642-4) into a series of numbers, such as “[0.32706347, 1.1575251, . . ., 1.5985818, 2.205582]” defining a sensor data numeric 644-7. In someembodiments, the sensor data numeric 644-7 may comprise two thousand andforty-eight (2048) numbers arranged in a series, matrix, etc.

In some embodiments, the method 600 may comprise conducting final AIprocessing, at “D”. The method 600 may, for example, utilize a claiminsights AI processing tool or module 642-5 that accepts each of thenumeric expressions 644-5, 644-6, 644-7 as inputs and conducts one ormore comparisons with stored numeric expressions representative ofprevious/historic queries/claims. The claim insights AI processingmodule 642-5 may, in some embodiments, mathematically combine and/oroperate upon the numeric expressions 644-5, 644-6, 644-7 for use in thecomparison. According to some embodiments, the numeric expressions644-5, 644-6, 644-7 may be combined by the claim insights AI processingmodule 642-5 into a single data string, stream, and/or expression, suchas “[0.08311458, 0.01241801, . . . , −0.13994503, 0.00641239,0.32706347, 1.1575251, . . . , 1.5985818, 2.205582, 0.018426, 0.000433,. . . , −0.003075, −0.024303]” which, in the ongoing non-limitingexample, may comprise three thousand five hundred and eighty-four (3584)numbers. In some embodiments, the result of the comparison(s) may beutilized to derive, calculate, generate, and/or otherwise compute queryand/or claim result data 644-8 as AI output, at “E”. As describedherein, for example, the claim result data 644-8 may comprise adetermination regarding whether a claim of loss should be paid (or not),and what amount should be paid (e.g., based on a damage estimate).

IV. Multivariate AI Smart Card Interfaces

Turning now to FIG. 7A, FIG. 7B, FIG. 7C, and FIG. 7D, diagrams of asystem 700 depicting a user device 702 providing instances of an exampleinterface 720 a-d according to some embodiments are shown. In someembodiments, the interface 720 a-d may comprise a web page, web form,database entry form, API, spreadsheet, table, and/or application orother GUI via which a user or other entity may enter data (e.g., provideor define input) to enable multivariate AI smart card processing and/oroutputting of automated query/claim results, as described herein. Theinterface 720 a-d may, for example, comprise a front-end of amultivariate AI smart card system client-side and/or mobile deviceapplication, program, and/or platform programmed and/or otherwiseconfigured to execute, conduct, and/or facilitate the methods 400, 500,600 of FIG. 4, FIG. 5, and/or FIG. 6 herein, and/or portions orcombinations thereof. In some embodiments, the interface 720 a-d may beoutput via a computerized device, such as the user device 702, which mayfor example, be similar in configuration to one or more of the userdevices 102 a-n, mobile electronic device 202, handheld device 402, thecontroller device 110, the servers 210, 410, the apparatus 810, and/orthe smart cards 106, 206 306, 406, of FIG. 1, FIG. 2, FIG. 3, FIG. 4,and/or FIG. 8 herein.

According to some embodiments, the interface 720 a-d may comprise one ormore tabs and/or other segmented and/or logically-presented data formsand/or fields. In some embodiments, the interface 720 a-d may beconfigured and/or organized to allow and/or facilitate entry and/orcapture of information defining a user query, query metadata, smart carddata, natural language data, image data, and/or other sensor data.According to some embodiments, the interface 720 a-d may comprise anautomated AI chat page via which the user may intuitively be guided toprovide the information required for AI processing of the user's query(e.g., claim submission). As depicted in FIG. 7A, for example, a firstversion (or page or instance) of the first version interface 720 a maycomprise a first chat message 722 a-1, such as the initial, welcome,and/or introductory message from an automated AI virtual assistant“Cari”. According to some embodiments, the first version of theinterface 720 a may comprise a second chat message 722 a-2 that providesa first query to the user, e.g., asking the user to provide informationdescriptive of the user's query/issue. According to some embodiments,each of the first and second chat messages 722 a-1, 722 a-2 may beautomatically generated by a multivariate AI smart card system (notshown) in response to receiving smart card data from the user device702, as described herein.

In some embodiments, the first version of the interface 720 a maycomprise a third chat message 722 a-3, such as a first response from theuser and/or a description of the user's query/claim of loss. As depictedin FIG. 7A, the user may provide input noting that the user's waterheater has failed and caused minor flooding damage in the user's home.According to some embodiments, the first version of the interface 720 amay comprise a fourth chat message 722 a-4, such as a first naturallanguage AI response. The natural language AI response may, for example,comprise an AI output constructed of natural language elements (e.g.,specifically chosen words) based on the user's description of thequery/loss as displayed in the third chat message 722 a-3. As describedherein, for example, the AI system may evaluate the content of the thirdchat message 722 a-3 to trigger an identification of an intent/issuepresented by the user, to identify and/or select an appropriatecontextual response, and to formulate the first natural language AIresponse as output via the fourth chat message 722 a-4. In someembodiments, the first version of the interface 720 a may comprise afifth chat message 722 a-5, such as a second natural language AIresponse. The second natural language AI response may comprise, forexample, a contextual query (e.g., a second query to the user) askingwhether the user would like to file/submit a claim for the loss. In someembodiments, the first version of the interface 720 a may comprise asixth chat message 722 a-6, such as a second response from the userindicating that the user would like to initiate a claims (and/or otherautomated AI decision-making result) process.

According to some embodiments, the first version of the interface 720 amay comprise a first text/chat entry area 724 a that comprises an inputmechanism (e.g., defining a first input mechanism) via which the usermay enter queries and/or responses that are transmitted to the AI smartcard system. In some embodiments, the first version of the interface 720a may comprise a first text/chat transmission button 726 a that triggersthe transmission upon receiving an indication (e.g., a click or touch)of activation or selection from the user.

Referring to FIG. 7B, a second version (or page or instance) of theinterface 720 b may comprise a continuation of the automated AI chatsession initiated in FIG. 7A. The second version of the interface 720 bmay comprise, for example, a seventh chat message 722 b-7 (only aportion of which is depicted in FIG. 7B) that is descriptive ofinformation obtained from a smart card and/or as a result of a metadataquery conducted by the AI system. In some embodiments, the automated AIvirtual assistant may trigger an outputting of the metadata to the uservia the seventh chat message 722 b-7 and may output an eighth chatmessage 722 b-8 comprising a query (e.g., a third query to the user)asking whether the metadata is correct. According to some embodiments,each of the seventh and eighth chat messages 722 b-7, 722 b-8 may beautomatically generated by the multivariate AI smart card system inresponse to receiving the smart card data from the user device 702, asdescribed herein.

In some embodiments, the second version of the interface 720 b maycomprise a ninth chat message 722 b-9, such as a third response from theuser, e.g., confirming the accuracy of the metadata (as depicted in FIG.7B) or providing edits/corrections, as the case may be. According tosome embodiments, the second version of the interface 720 b may comprisea tenth chat message 722 b-10, such as a third natural language AIresponse. The natural language AI response may, for example, comprise anAI output constructed of natural language elements (e.g., specificallychosen words) based on the user's answer to the third user query asprovided by the user in the ninth chat message 722 b-9. The AI systemmay evaluate the content of the ninth chat message 722 b-9, for example,to trigger an identification of an intent/issue presented by the user,to identify and/or select an appropriate contextual response, and toformulate the third natural language AI response as output via the tenthchat message 722 b-10 (e.g., confirming receipt and/or acknowledgementof the user's answer). In some embodiments, the second version of theinterface 720 b may comprise an eleventh chat message 722 b-11, such asa fourth natural language AI response. The fourth natural language AIresponse may comprise, for example, a contextual query (e.g., a fourthquery to the user) asking for more specific details descriptive of theevent/loss. In some embodiments, the second version of the interface 720b may comprise a twelfth chat message 722 b-12, such as a fourthresponse from the user that provides additional textual (and/or othermultimedia, such as voice) details descriptive of the event/loss.

According to some embodiments, the second version of the interface 720 bmay comprise a thirteenth chat message 722 b-13, such as a fifth naturallanguage AI response. The natural language AI response may, for example,comprise an AI output constructed of natural language elements (e.g.,specifically chosen words) based on the user's answer to the fourth userquery as provided by the user in the twelfth chat message 722 b-12. TheAI system may evaluate the content of the twelfth chat message 722 b-12,for example, to trigger an identification of an intent/issue presentedby the user, to identify and/or select an appropriate contextualresponse, and to formulate the fifth natural language AI response asoutput via the thirteenth chat message 722 b-13 (e.g., confirmingreceipt and/or acknowledgement of the user's answer). In someembodiments, the second version of the interface 720 b may comprise afourteenth chat message 722 b-14 and/or a fifteenth chat message 722b-15, either or both of which may comprise a sixth natural language AIresponse. The sixth natural language AI response may comprise, forexample, contextual data and/or a query (e.g., a fifth query to theuser) directed to acquiring images (and/or other sensor data)descriptive of the event/loss. In some embodiments, the sixth naturallanguage AI response(s) may provide specific directions for acquiringthe requested data such as the type of picture requested (e.g., a “closeup” picture), quantity of images, list of images, type of data/sensorreading requested, and/or instructions for how to acquire/capture therequested data (e.g., instructions regarding activation of a particularsensor device (not shown) via the user device 702). According to someembodiments, the second version of the interface 720 b may comprise asixteenth chat message 722 b-16, such as a fifth response from the userthat provides authorization for the acquisition of the requestedimages/data. The fifth response may, for example, comprise anauthorization from the user that permits the AI system to take controlof the camera/sensor of (or coupled to) the user device 702.

In some embodiments, the user and/or the AI system may capture a firstimage 728 b that is output via the second version of the interface 720 b(and/or transmitted to the AI system for analysis). The first image 728b may, for example, comprise an image of a leaking/damaged water heaterin the user's basement, as originally textually described by the user inthe user's description of the query/loss as displayed in the third chatmessage 722 a-3 of the first version of the interface 720 a in FIG. 7A.According to some embodiments, the second version of the interface 720 bmay comprise a second text/chat entry area 724 b that comprises an inputmechanism (e.g., defining a second input mechanism) via which the usermay enter queries and/or responses that are transmitted to the AI smartcard system. In some embodiments, the second version of the interface720 b may comprise a second text/chat transmission button 726 b thattriggers the transmission upon receiving an indication (e.g., a click ortouch) of activation or selection from the user.

Referring to FIG. 7C, a third version (or page or instance) of theinterface 720 c may comprise a continuation of the automated AI chatsession of FIG. 7A and/or FIG. 7B. The third version of the interface720 c may comprise, for example, a seventeenth chat message 722 c-17,such as a sixth response from the user, e.g., asking the AI systemwhether the submitted image/data (e.g., the first image 728 b) isacceptable. According to some embodiments, the third version of theinterface 720 c may comprise an eighteenth chat message 722 c-18, suchas a seventh natural language AI response. The natural language AIresponse may, for example, comprise an AI output constructed of naturallanguage elements (e.g., specifically chosen words) based on the sixthresponse from the user provided by the user in the seventeenth chatmessage 722 c-17 and/or based on the captured image/data (e.g., thefirst image 728 b). The AI system may evaluate the content of theseventeenth chat message 722 c-17 and/or the first image 728 b, forexample, to trigger an evaluation and/or analysis of the first image 728b, to identify an adequacy of information derived from the first image728 b, to identify and/or select an appropriate contextual response, andto formulate the seventh natural language AI response as output via theeighteenth chat message 722 c-18 (e.g., acknowledging that the firstimage 728 b is acceptable, as depicted, or requesting additionalimages/sensor data, as the case may be). In some embodiments, the thirdversion of the interface 720 c may comprise a nineteenth chat message722 c-19, such as an eighth natural language AI response. The eighthnatural language AI response may comprise, for example, a contextualquery (e.g., a sixth query to the user) asking if the user would like toshare any additional images/data descriptive of the event/loss. In someembodiments, the third version of the interface 720 c may comprise atwentieth chat message 722 c-20, such as a seventh response from theuser that provides additional textual (and/or other multimedia, such asvoice) details descriptive of the event/loss and/or provides a secondimage 728 c.

According to some embodiments, the third version of the interface 720 cmay comprise a twenty-first chat message 722 c-21, such as a ninthnatural language AI response. The natural language AI response may, forexample, comprise an AI output constructed of natural language elements(e.g., specifically chosen words) based on the second image 728 c. TheAI system may evaluate the second image 728 c, for example, to triggeran analysis of the second image 728 c, to identify an adequacy ofinformation derived from the second image 728 c, to identify and/orselect an appropriate contextual response, and to formulate the ninthnatural language AI response as output via the twenty-first chat message722 c-21 (e.g., confirming an adequacy of the second image 728 c and/orasking the user whether the user would like to protect their propertyfrom further damage). In some embodiments, the third version of theinterface 720 c may comprise twenty-second chat message 722 c-22, whichmay comprise an eighth response from the user. The eighth response maycomprise, for example, a request for additional information and/or anaffirmation that the user would like to learn how to avoid additionaldamage). According to some embodiments, the AI system may then guide theuser (not shown) utilizing how-to videos, continued chat sessiondetails, checklists, etc.

In some embodiments, the third version of the interface 720 c maycomprise a third text/chat entry area 724 c that comprises an inputmechanism (e.g., defining a third input mechanism) via which the usermay enter queries and/or responses that are transmitted to the AI smartcard system. In some embodiments, the third version of the interface 720c may comprise a third text/chat transmission button 726 c that triggersthe transmission upon receiving an indication (e.g., a click or touch)of activation or selection from the user.

Referring to FIG. 7D, a fourth version (or page or instance) of theinterface 720 d may comprise a continuation of the automated AI chatsession of FIG. 7A, FIG. 7B, and/or FIG. 7C, and/or may comprise aseparate chat session. The fourth version of the interface 720 d maycomprise, for example, an automated damage estimation and/or query/claimresult platform that offers resolution to the user's query/claim.According to some embodiments, the fourth version of the interface 720 dmay comprise a twenty-third chat message 722 d-23, such as a tenthnatural language AI response. The natural language AI response may, forexample, comprise an AI output constructed of natural language elements(e.g., specifically chosen words) based on an AI analysis of capturedimages/data and/or a comparison of the captured/acquired data toprestored reference images/data. In some embodiments, the damageestimate may be based on pre-recorded damage figures for previousevents/losses that are identified as being similar to the currentevent/loss (e.g., based on multivariate AI smart card analysis asdescribed herein). According to some embodiments, the fourth version ofthe interface 720 d may comprise a twenty-fourth chat message 722 d-24,such as a ninth response from the user (e.g., expressing surprise and/orconcern regarding the damage estimate value). In some embodiments, thefourth version of the interface 720 d may comprise a twenty-fifth chatmessage 722 d-25, such as an eleventh natural language AI response. TheAI system may evaluate the content of the twenty-fourth chat message 722d-24, for example, to trigger an evaluation and/or analysis of the ninthresponse, to identify and/or select an appropriate contextual response,and to formulate the eleventh natural language AI response as output viathe twenty-fifth chat message 722 d-25 (e.g., acknowledging the user'sconcern by providing detailed and/or applicable information regardingthe user's deductible).

In some embodiments, the fourth version of the interface 720 d maycomprise a twenty-sixth chat message 722 d-26, such as a tenth responsefrom the user (e.g., expressing thanks and/or relief). According to someembodiments, the fourth version of the interface 720 d may comprise atwenty-seventh chat message 722 d-27, such as a twelfth natural languageAI response. The AI system may evaluate the content of the twenty-sixthchat message 722 d-26, for example, to trigger an evaluation and/oranalysis of the tenth response, to identify and/or select an appropriatecontextual response, and to formulate the twelfth natural language AIresponse as output via the twenty-seventh chat message 722 d-27 (e.g.,acknowledging the user's response). According to some embodiments, thetwelfth natural language AI response may also or alternatively comprisea contextual query (e.g., a seventh query to the user) asking if theuser would like to details regarding the cause of loss and/or regardinghow to prevent additional losses. In some embodiments, the fourthversion of the interface 720 d may comprise a twenty-eighth chat message722 d-28, such as an eleventh response from the user that indicates aninterest in obtaining additional data and/or guidance. In someembodiments, the fourth version of the interface 720 d may comprise atwenty-ninth chat message 722 d-29, such as a thirteenth naturallanguage AI response that acknowledge the user's desire and notifies theuser regarding the additional information/guidance.

In some embodiments, the fourth version of the interface 720 d maycomprise a fourth text/chat entry area 724 d that comprises an inputmechanism (e.g., defining a fourth input mechanism) via which the usermay enter queries and/or responses that are transmitted to the AI smartcard system. In some embodiments, the fourth version of the interface720 d may comprise a fourth text/chat transmission button 726 d thattriggers the transmission upon receiving an indication (e.g., a click ortouch) of activation or selection from the user.

While various components of the interface 720 a-d have been depictedwith respect to certain labels, layouts, headings, titles, contexts,relationships, and/or configurations, these features have been presentedfor reference and example only. Other labels, layouts, headings, titles,contexts, relationships, and/or configurations may be implementedwithout deviating from the scope of embodiments herein. Similarly, whilea certain number of tabs, information screens, form fields, chatmessages, and/or data entry options have been presented, variationsthereof may be practiced in accordance with some embodiments.

V. Multivariate AI Smart Card Apparatus & Articles of Manufacture

Turning to FIG. 8, a block diagram of an apparatus 810 according to someembodiments is shown. In some embodiments, the apparatus 810 may besimilar in configuration and/or functionality to one or more of the userdevices 102 a-n, mobile electronic device 202, handheld device 402, thecontroller device 110, the servers 210, 410, and/or the smart cards 106,206 306, 406, of FIG. 1, FIG. 2, FIG. 3, and/or FIG. 4 herein. Theapparatus 810 may, for example, execute, process, facilitate, and/orotherwise be associated with the methods 400, 500, 600 of FIG. 4, FIG.5, and/or FIG. 6 herein, and/or portions or combinations thereof. Insome embodiments, the apparatus 810 may comprise a processing device812, an input device 814, an output device 816, a communication device818, an interface 820, a memory device 840 (storing various programsand/or instructions 842 and data 844), and/or a cooling device 850.According to some embodiments, any or all of the components 812, 814,816, 818, 820, 840, 842, 844, 850 of the apparatus 810 may be similar inconfiguration and/or functionality to any similarly named and/ornumbered components described herein. Fewer or more components 812, 814,816, 818, 820, 840, 842, 844, 850 and/or various configurations of thecomponents 812, 814, 816, 818, 820, 840, 842, 844, 850 be included inthe apparatus 810 without deviating from the scope of embodimentsdescribed herein.

According to some embodiments, the processor 812 may be or include anytype, quantity, and/or configuration of processor that is or becomesknown. The processor 812 may comprise, for example, an Intel® IXP 2800network processor or an Intel® XEON™ Processor coupled with an Intel®E7501 chipset. In some embodiments, the processor 812 may comprisemultiple inter-connected processors, microprocessors, and/ormicro-engines. According to some embodiments, the processor 812 (and/orthe apparatus 810 and/or other components thereof) may be supplied powervia a power supply (not shown) such as a battery, an AI ternatingCurrent (AC) source, a Direct Current (DC) source, an AC/DC adapter,solar cells, and/or an inertial generator. In the case that theapparatus 810 comprises a server such as a blade server or a virtualco-location device, necessary power may be supplied via a standard ACoutlet, power strip, surge protector, and/or Uninterruptible PowerSupply (UPS) device.

In some embodiments, the input device 814 and/or the output device 816are communicatively coupled to the processor 812 (e.g., via wired and/orwireless connections and/or pathways) and they may generally compriseany types or configurations of input and output components and/ordevices that are or become known, respectively. The input device 814 maycomprise, for example, a keyboard that allows an operator of theapparatus 810 to interface with the apparatus 810 (e.g., by anadministrator, such as to setup and/or configure a multivariate AI smartcard processing system, as described herein). In some embodiments, theinput device 814 may comprise a sensor, such as a camera, sound, light,and/or proximity sensor configured to measure parameter values andreport measured values via signals to the apparatus 810 and/or theprocessor 812. The output device 816 may, according to some embodiments,comprise a display screen and/or other practicable output componentand/or device. The output device 816 may, for example, provide aninterface (such as the interface 220 and/or the interface 720 a-d ofFIG. 2, FIG. 7A, FIG. 7B, FIG. 7C, and/or FIG. 7D herein) via whichmultivariate AI smart card processing and/or query result determinationfunctionality is provided to a user (e.g., via a website and/or mobileapplication). According to some embodiments, the input device 814 and/orthe output device 816 may comprise and/or be embodied in a single devicesuch as a touch-screen monitor.

In some embodiments, the communication device 818 may comprise any typeor configuration of communication device that is or becomes known orpracticable. The communication device 818 may, for example, comprise aNetwork Interface Card (NIC), a telephonic device, a cellular networkdevice, a router, a hub, a modem, and/or a communications port or cable.In some embodiments, the communication device 818 may be coupled toreceive smart card and/or local environment sensor data and/or forwardsuch data to one or more other (e.g., remote) devices (not shown in FIG.8). The communication device 818 may, for example, comprise a BLE and/orRF receiver device and/or a camera or other imaging device that acquiresdata from a smart card (not separately depicted in FIG. 8) and/or atransmitter device that provides the data to a remote server (also notseparately shown in FIG. 8). According to some embodiments, thecommunication device 818 may also or alternatively be coupled to theprocessor 812. In some embodiments, the communication device 818 maycomprise an IR, RF, Bluetooth™, Near-Field Communication (NFC), and/orWi-Fi® network device coupled to facilitate communications between theprocessor 812 and another device (such as a remote user device and/or asmart card device, not separately shown in FIG. 8).

The memory device 840 may comprise any appropriate information storagedevice that is or becomes known or available, including, but not limitedto, units and/or combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, and/or semiconductor memorydevices such as RAM devices, Read Only Memory (ROM) devices, Single DataRate Random Access Memory (SDR-RAM), Double Data Rate Random AccessMemory (DDR-RAM), and/or Programmable Read Only Memory (PROM). Thememory device 840 may, according to some embodiments, store one or moreof AI metadata instructions 842-1, AI sensor data instructions 842-2, AIlanguage data instructions 842-3, metadata data 844-1, sensor data844-2, and/or language data 844-3. In some embodiments, the AI metadatainstructions 842-1, AI sensor data instructions 842-2, AI language datainstructions 842-3 may be utilized by the processor 812 to provideoutput information via the output device 816 and/or the communicationdevice 818.

According to some embodiments, the AI metadata instructions 842-1 may beoperable to cause the processor 812 to process the metadata data 844-1,sensor data 844-2, and/or language data 844-3 in accordance withembodiments as described herein. Metadata data 844-1, sensor data 844-2,and/or language data 844-3 received via the input device 814 and/or thecommunication device 818 may, for example, be analyzed, sorted,filtered, decoded, decompressed, ranked, scored, plotted, and/orotherwise processed by the processor 812 in accordance with the AImetadata instructions 842-1. In some embodiments, metadata data 844-1,sensor data 844-2, and/or language data 844-3 may be fed by theprocessor 812 through one or more mathematical, AI logic (e.g., neuralnetwork), and/or statistical formulas and/or models in accordance withthe AI metadata instructions 842-1 to convert the metadata 844-1 (e.g.,smart card and/or related data) to one or more metadata numeric valuesand/or to identify similar user queries/claims of loss based on acomparison of the one or more metadata numeric values with a pluralityof stored metadata numeric values for other/previous matters, asdescribed herein.

In some embodiments, the AI sensor data instructions 842-2 may beoperable to cause the processor 812 to process the metadata data 844-1,sensor data 844-2, and/or language data 844-3 in accordance withembodiments as described herein. Metadata data 844-1, sensor data 844-2,and/or language data 844-3 received via the input device 814 and/or thecommunication device 818 may, for example, be analyzed, sorted,filtered, decoded, decompressed, ranked, scored, plotted, and/orotherwise processed by the processor 812 in accordance with the AIsensor data instructions 842-2. In some embodiments, metadata data844-1, sensor data 844-2, and/or language data 844-3 may be fed by theprocessor 812 through one or more mathematical, AI logic (e.g., neuralnetwork), and/or statistical formulas and/or models in accordance withthe AI sensor data instructions 842-2 to convert the sensor data 844-2(e.g., image and/or other sensor data) to one or more sensor datanumeric values and/or to identify similar user queries/claims of lossbased on a comparison of the one or more sensor data numeric values witha plurality of stored sensor data numeric values for other/previousmatters, as described herein.

According to some embodiments, the AI language data instructions 842-3may be operable to cause the processor 812 to process the metadata data844-1, sensor data 844-2, and/or language data 844-3 in accordance withembodiments as described herein. Metadata data 844-1, sensor data 844-2,and/or language data 844-3 received via the input device 814 and/or thecommunication device 818 may, for example, be analyzed, sorted,filtered, decoded, decompressed, ranked, scored, plotted, and/orotherwise processed by the processor 812 in accordance with the AIlanguage data instructions 842-3. In some embodiments, metadata data844-1, sensor data 844-2, and/or language data 844-3 may be fed by theprocessor 812 through one or more mathematical, AI logic (e.g., neuralnetwork), and/or statistical formulas and/or models in accordance withthe AI language data instructions 842-3 to convert the language data844-3 (e.g., text, audio, and/or other voice data) to one or morelanguage data numeric values and/or to identify similar userqueries/claims of loss based on a comparison of the one or more languagedata numeric values with a plurality of stored language data numericvalues for other/previous matters, as described herein.

According to some embodiments, the apparatus 810 may comprise thecooling device 850. According to some embodiments, the cooling device850 may be coupled (physically, thermally, and/or electrically) to theprocessor 812 and/or to the memory device 840. The cooling device 850may, for example, comprise a fan, heat sink, heat pipe, radiator, coldplate, and/or other cooling component or device or combinations thereof,configured to remove heat from portions or components of the apparatus810.

Any or all of the exemplary instructions and data types described hereinand other practicable types of data may be stored in any number, type,and/or configuration of memory devices that is or becomes known. Thememory device 840 may, for example, comprise one or more data tables orfiles, databases, table spaces, registers, and/or other storagestructures. In some embodiments, multiple databases and/or storagestructures (and/or multiple memory devices 840) may be utilized to storeinformation associated with the apparatus 810. According to someembodiments, the memory device 840 may be incorporated into and/orotherwise coupled to the apparatus 810 (e.g., as shown) or may simply beaccessible to the apparatus 810 (e.g., externally located and/orsituated).

Referring to FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, and FIG. 9E,perspective diagrams of exemplary data storage devices 940 a-e accordingto some embodiments are shown. The data storage devices 940 a-e may, forexample, be utilized to store instructions and/or data such as the AImetadata instructions 842-1, AI sensor data instructions 842-2, AIlanguage data instructions 842-3, metadata data 844-1, sensor data844-2, and/or language data 844-3, each of which is presented inreference to FIG. 8 herein. In some embodiments, instructions stored onthe data storage devices 740 a-e may, when executed by a processor,cause the implementation of and/or facilitate the methods 400, 500, 600of FIG. 4, FIG. 5, and/or FIG. 6 herein, and/or portions or combinationsthereof.

According to some embodiments, the first data storage device 940 a maycomprise one or more various types of internal and/or external harddrives. The first data storage device 940 a may, for example, comprise adata storage medium 946 that is read, interrogated, and/or otherwisecommunicatively coupled to and/or via a disk reading device 948. In someembodiments, the first data storage device 940 a and/or the data storagemedium 946 may be configured to store information utilizing one or moremagnetic, inductive, and/or optical means (e.g., magnetic, inductive,and/or optical-encoding). The data storage medium 946, depicted as afirst data storage medium 946 a for example (e.g., breakoutcross-section “A”), may comprise one or more of a polymer layer 946 a-1,a magnetic data storage layer 946 a-2, a non-magnetic layer 946 a-3, amagnetic base layer 946 a-4, a contact layer 946 a-5, and/or a substratelayer 946 a-6. According to some embodiments, a magnetic read head 948 amay be coupled and/or disposed to read data from the magnetic datastorage layer 946 a-2.

In some embodiments, the data storage medium 946, depicted as a seconddata storage medium 946 b for example (e.g., breakout cross-section“B”), may comprise a plurality of data points 946 b-2 disposed with thesecond data storage medium 946 b. The data points 946 b-2 may, in someembodiments, be read and/or otherwise interfaced with via alaser-enabled read head 948 b disposed and/or coupled to direct a laserbeam through the second data storage medium 946 b.

In some embodiments, the second data storage device 940 b may comprise aCD, CD-ROM, DVD, Blu-Ray™ Disc, and/or other type of optically-encodeddisk and/or other storage medium that is or becomes know or practicable.In some embodiments, the third data storage device 940 c may comprise aUSB keyfob, dongle, and/or other type of flash memory data storagedevice that is or becomes know or practicable. In some embodiments, thefourth data storage device 940 d may comprise RAM of any type, quantity,and/or configuration that is or becomes practicable and/or desirable. Insome embodiments, the fourth data storage device 940 d may comprise anoff-chip cache such as a Level 2 (L2) cache memory device. According tosome embodiments, the fifth data storage device 940 e may comprise anon-chip memory device such as a Level 1 (L1) cache memory device.

The data storage devices 940 a-e may generally store programinstructions, code, and/or modules that, when executed by a processingdevice cause a particular machine to function in accordance with one ormore embodiments described herein. The data storage devices 940 a-edepicted in FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, and FIG. 9E arerepresentative of a class and/or subset of computer-readable media thatare defined herein as “computer-readable memory” (e.g., non-transitorymemory devices as opposed to transmission devices or media).

VI. Rules of Interpretation

Throughout the description herein and unless otherwise specified, thefollowing terms may include and/or encompass the example meaningsprovided. These terms and illustrative example meanings are provided toclarify the language selected to describe embodiments both in thespecification and in the appended claims, and accordingly, are notintended to be generally limiting. While not generally limiting andwhile not limiting for all described embodiments, in some embodiments,the terms are specifically limited to the example definitions and/orexamples provided. Other terms are defined throughout the presentdescription.

Some embodiments described herein are associated with a “user device” ora “network device”. As used herein, the terms “user device” and “networkdevice” may be used interchangeably and may generally refer to anydevice that can communicate via a network. Examples of user or networkdevices include a PC, a workstation, a server, a printer, a scanner, afacsimile machine, a copier, a Personal Digital Assistant (PDA), astorage device (e.g., a disk drive), a hub, a router, a switch, and amodem, a video game console, or a wireless phone. User and networkdevices may comprise one or more communication or network components. Asused herein, a “user” may generally refer to any individual and/orentity that operates a user device. Users may comprise, for example,customers, consumers, product underwriters, product distributors,customer service representatives, agents, brokers, etc.

As used herein, the term “network component” may refer to a user ornetwork device, or a component, piece, portion, or combination of useror network devices. Examples of network components may include a StaticRandom Access Memory (SRAM) device or module, a network processor, and anetwork communication path, connection, port, or cable.

In addition, some embodiments are associated with a “network” or a“communication network”. As used herein, the terms “network” and“communication network” may be used interchangeably and may refer to anyobject, entity, component, device, and/or any combination thereof thatpermits, facilitates, and/or otherwise contributes to or is associatedwith the transmission of messages, packets, signals, and/or other formsof information between and/or within one or more network devices.Networks may be or include a plurality of interconnected networkdevices. In some embodiments, networks may be hard-wired, wireless,virtual, neural, and/or any other configuration of type that is orbecomes known. Communication networks may include, for example, one ormore networks configured to operate in accordance with the Fast EthernetLAN transmission standard 802.3-2002® published by the Institute ofElectrical and Electronics Engineers (IEEE). In some embodiments, anetwork may include one or more wired and/or wireless networks operatedin accordance with any communication standard or protocol that is orbecomes known or practicable.

As used herein, the terms “information” and “data” may be usedinterchangeably and may refer to any data, text, voice, video, image,message, bit, packet, pulse, tone, waveform, and/or other type orconfiguration of signal and/or information. Information may compriseinformation packets transmitted, for example, in accordance with theInternet Protocol Version 6 (IPv6) standard as defined by “InternetProtocol Version 6 (IPv6) Specification” RFC 1883, published by theInternet Engineering Task Force (IETF), Network Working Group, S.Deering et al. (December 1995). Information may, according to someembodiments, be compressed, encoded, encrypted, and/or otherwisepackaged or manipulated in accordance with any method that is or becomesknown or practicable.

In addition, some embodiments described herein are associated with an“indication”. As used herein, the term “indication” may be used to referto any indicia and/or other information indicative of or associated witha subject, item, entity, and/or other object and/or idea. As usedherein, the phrases “information indicative of” and “indicia” may beused to refer to any information that represents, describes, and/or isotherwise associated with a related entity, subject, or object. Indiciaof information may include, for example, a code, a reference, a link, asignal, an identifier, and/or any combination thereof and/or any otherinformative representation associated with the information. In someembodiments, indicia of information (or indicative of the information)may be or include the information itself and/or any portion or componentof the information. In some embodiments, an indication may include arequest, a solicitation, a broadcast, and/or any other form ofinformation gathering and/or dissemination.

Numerous embodiments are described in this patent application, and arepresented for illustrative purposes only. The described embodiments arenot, and are not intended to be, limiting in any sense. The presentlydisclosed invention(s) are widely applicable to numerous embodiments, asis readily apparent from the disclosure. One of ordinary skill in theart will recognize that the disclosed invention(s) may be practiced withvarious modifications and alterations, such as structural, logical,software, and electrical modifications. AI though particular features ofthe disclosed invention(s) may be described with reference to one ormore particular embodiments and/or drawings, it should be understoodthat such features are not limited to usage in the one or moreparticular embodiments or drawings with reference to which they aredescribed, unless expressly specified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. On the contrary, such devices need only transmit to eachother as necessary or desirable, and may actually refrain fromexchanging data most of the time. For example, a machine incommunication with another machine via the Internet may not transmitdata to the other machine for weeks at a time. In addition, devices thatare in communication with each other may communicate directly orindirectly through one or more intermediaries.

A description of an embodiment with several components or features doesnot imply that all or even any of such components and/or features arerequired. On the contrary, a variety of optional components aredescribed to illustrate the wide variety of possible embodiments of thepresent invention(s). Unless otherwise specified explicitly, nocomponent and/or feature is essential or required.

Further, although process steps, algorithms or the like may be describedin a sequential order, such processes may be configured to work indifferent orders. In other words, any sequence or order of steps thatmay be explicitly described does not necessarily indicate a requirementthat the steps be performed in that order. The steps of processesdescribed herein may be performed in any order practical. Further, somesteps may be performed simultaneously despite being described or impliedas occurring non-simultaneously (e.g., because one step is describedafter the other step). Moreover, the illustration of a process by itsdepiction in a drawing does not imply that the illustrated process isexclusive of other variations and modifications thereto, does not implythat the illustrated process or any of its steps are necessary to theinvention, and does not imply that the illustrated process is preferred.

“Determining” something can be performed in a variety of manners andtherefore the term “determining” (and like terms) includes calculating,computing, deriving, looking up (e.g., in a table, database or datastructure), ascertaining and the like.

It will be readily apparent that the various methods and algorithmsdescribed herein may be implemented by, e.g., appropriately and/orspecially-programmed computers and/or computing devices. Typically aprocessor (e.g., one or more microprocessors) will receive instructionsfrom a memory or like device, and execute those instructions, therebyperforming one or more processes defined by those instructions. Further,programs that implement such methods and algorithms may be stored andtransmitted using a variety of media (e.g., computer readable media) ina number of manners. In some embodiments, hard-wired circuitry or customhardware may be used in place of, or in combination with, softwareinstructions for implementation of the processes of various embodiments.Thus, embodiments are not limited to any specific combination ofhardware and software

A “processor” generally means any one or more microprocessors, CPUdevices, computing devices, microcontrollers, digital signal processors,or like devices, as further described herein.

The term “computer-readable medium” refers to any medium thatparticipates in providing data (e.g., instructions or other information)that may be read by a computer, a processor or a like device. Such amedium may take many forms, including but not limited to, non-volatilemedia, volatile media, and transmission media. Non-volatile mediainclude, for example, optical or magnetic disks and other persistentmemory. Volatile media include DRAM, which typically constitutes themain memory. Transmission media include coaxial cables, copper wire andfiber optics, including the wires that comprise a system bus coupled tothe processor. Transmission media may include or convey acoustic waves,light waves and electromagnetic emissions, such as those generatedduring RF and IR data communications. Common forms of computer-readablemedia include, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any othermemory chip or cartridge, a carrier wave, or any other medium from whicha computer can read.

The term “computer-readable memory” may generally refer to a subsetand/or class of computer-readable medium that does not includetransmission media, such as waveforms, carrier waves, electromagneticemissions, etc. Computer-readable memory may typically include physicalmedia upon which data (e.g., instructions or other information) arestored, such as optical or magnetic disks and other persistent memory,DRAM, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD, any other optical medium, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip orcartridge, computer hard drives, backup tapes, Universal Serial Bus(USB) memory devices, and the like.

Various forms of computer readable media may be involved in carryingdata, including sequences of instructions, to a processor. For example,sequences of instruction (i) may be delivered from RAM to a processor,(ii) may be carried over a wireless transmission medium, and/or (iii)may be formatted according to numerous formats, standards or protocols,such as Bluetooth™, TDMA, CDMA, 3G.

Where databases are described, it will be understood by one of ordinaryskill in the art that (i) alternative database structures to thosedescribed may be readily employed, and (ii) other memory structuresbesides databases may be readily employed. Any illustrations ordescriptions of any sample databases presented herein are illustrativearrangements for stored representations of information. Any number ofother arrangements may be employed besides those suggested by, e.g.,tables illustrated in drawings or elsewhere. Similarly, any illustratedentries of the databases represent exemplary information only; one ofordinary skill in the art will understand that the number and content ofthe entries can be different from those described herein. Further,despite any depiction of the databases as tables, other formats(including relational databases, object-based models and/or distributeddatabases) could be used to store and manipulate the data typesdescribed herein. Likewise, object methods or behaviors of a databasecan be used to implement various processes, such as the describedherein. In addition, the databases may, in a known manner, be storedlocally or remotely from a device that accesses data in such a database.

The present invention can be configured to work in a network environmentincluding a computer that is in communication, via a communicationsnetwork, with one or more devices. The computer may communicate with thedevices directly or indirectly, via a wired or wireless medium such asthe Internet, LAN, WAN or Ethernet, Token Ring, or via any appropriatecommunications means or combination of communications means. Each of thedevices may comprise computers, such as those based on the Intel®Pentium® or Centrino™ processor, that are adapted to communicate withthe computer. Any number and type of machines may be in communicationwith the computer.

The present disclosure provides, to one of ordinary skill in the art, anenabling description of several embodiments and/or inventions. Some ofthese embodiments and/or inventions may not be claimed in the presentapplication, but may nevertheless be claimed in one or more continuingapplications that claim the benefit of priority of the presentapplication. Applicants intend to file additional applications to pursuepatents for subject matter that has been disclosed and enabled but notclaimed in the present application.

It will be understood that various modifications can be made to theembodiments of the present disclosure herein without departing from thescope thereof. Therefore, the above description should not be construedas limiting the disclosure, but merely as embodiments thereof. Thoseskilled in the art will envision other modifications within the scope ofthe invention as defined by the claims appended hereto.

What is claimed is:
 1. A system for automatically classifying multimediaclaim data utilizing Artificial Intelligence (AI), comprising: anelectronic processing device; and a non-transitory computer-readablememory device storing instructions comprising (i) a claim similarity AImodel, (ii) a claim severity AI model, (iii) a claim pricing AI model,and (iv) an AI chat module, wherein execution of the instructions by theelectronic processing device results in: receiving, from a mobileapplication of a mobile electronic device of an insured party and as aninitiation of a process to submit a current insurance claim, anindication of computer-readable data stored on a smart card;identifying, by the electronic processing device and utilizing the datastored on the smart card, a two-factor authentication challenge;transmitting, to the mobile electronic device of the insured party andin response to the identifying of the two-factor authenticationchallenge, the two-factor authentication challenge; receiving, from themobile electronic device of the insured party, an indication of aresponse to the two-factor authentication challenge; authenticating, bythe electronic processing device and utilizing the response to thetwo-factor authentication challenge, the insured party; converting, byan execution of the claim similarity AI model by the electronicprocessing device, the data stored on the smart card into a firstnumber; receiving, from the mobile application of the mobile electronicdevice of the insured party, an indication of sensor data obtained bythe mobile electronic device; converting, by an execution of at leastone of the claim similarity AI model and the claim severity AI model bythe electronic processing device, the sensor data into a second number;generating, by an execution of the AI chat module by the electronicprocessing device and based at least in part on the data stored on thesmart card, at least one chat query; receiving, from the mobileapplication of the mobile electronic device of the insured party and inresponse to the generating of the at least one chat query, an indicationof a chat response from the insured party; converting, by an executionof at least one of the claim similarity AI model and the claim severityAI model by the electronic processing device, the chat response into athird number; comparing, by the electronic processing device, utilizingthe first, second, and third numbers, and by accessing a databasestoring data descriptive of a plurality of historic claims data, thecurrent insurance claim to previous insurance claims; identifying, bythe electronic processing device and based on the comparing, at leastone previous insurance claim that is numerically similar to the currentinsurance claim within a predefined confidence threshold; computing, byan execution of the claim pricing AI model by the electronic processingdevice and utilizing claim payment data stored with respect to the atleast one previous insurance claim, a resolution for the currentinsurance claim; and transmitting, to the mobile electronic device ofthe insured party and in response to the computing of the resolution forthe current insurance claim, a visual indication of the resolution forthe current insurance claim.
 2. The system for automatically classifyingmultimedia claim data utilizing AI of claim 1, wherein the instructionsfurther comprise: (v) an AI fraud detection model.
 3. The system forautomatically classifying multimedia claim data utilizing AI of claim 2,wherein the execution of the instructions by the electronic processingdevice further results in: comparing, by an execution of the AI frauddetection model by the electronic processing device, utilizing at leastone of the first, second, and third numbers, and by accessing thedatabase storing data descriptive of the plurality of historic claimsdata, the current insurance claim to previous insurance claims in whichfraud had occurred; and computing, by the execution of the AI frauddetection model by the electronic processing device and based on thecomparing of the current insurance claim to the previous insuranceclaims in which fraud had occurred, a statistical likelihood that thecurrent insurance claim is fraudulent.
 4. The system for automaticallyclassifying multimedia claim data utilizing AI of claim 1, wherein theinstructions further comprise: (v) a cause of loss AI model.
 5. Thesystem for automatically classifying multimedia claim data utilizing AIof claim 4, wherein the execution of the instructions by the electronicprocessing device further results in: comparing, by an execution of thecause of loss AI model by the electronic processing device, utilizing atleast one of the first, second, and third numbers, and by accessing thedatabase storing data descriptive of the plurality of historic claimsdata, the current insurance claim to previous insurance claims in whicha cause of loss was identified; and computing, by the execution of thecause of loss AI model by the electronic processing device and based onthe comparing of the current insurance claim to previous insuranceclaims in which a cause of loss was identified, a cause of loss for thecurrent insurance claim.
 6. The system for automatically classifyingmultimedia claim data utilizing AI of claim 1, wherein thecomputer-readable data stored on the smart card is stored in atwo-dimensional bar code.
 7. The system for automatically classifyingmultimedia claim data utilizing AI of claim 1, wherein thecomputer-readable data stored on the smart card is stored in a memorydevice accessible by near-field communication interrogation.
 8. Thesystem for automatically classifying multimedia claim data utilizing AIof claim 1, wherein the computer-readable data stored on the smart cardcomprises at least one of a name of the insured party, an address of theinsured party, a policy number of the insured party, and a vehicleidentifier of a vehicle owned by the insured party.
 9. The system forautomatically classifying multimedia claim data utilizing AI of claim 1,wherein the response to the two-factor authentication challengecomprises a voice imprint.
 10. The system for automatically classifyingmultimedia claim data utilizing AI of claim 1, wherein the response tothe two-factor authentication challenge comprises a facial image. 11.The system for automatically classifying multimedia claim data utilizingAI of claim 1, wherein the sensor data comprises at least one of imageand video data.
 12. The system for automatically classifying multimediaclaim data utilizing AI of claim 1, wherein the sensor data comprisesdata captured from an accelerometer of the mobile electronic device. 13.The system for automatically classifying multimedia claim data utilizingAI of claim 1, wherein the sensor data comprises data captured from asensor coupled to a home monitoring system within a home of the insuredparty.
 14. The system for automatically classifying multimedia claimdata utilizing AI of claim 1, wherein the sensor data comprises dataacquired from a third-party device.
 15. A system for automaticallyclassifying multimedia claim data utilizing Artificial Intelligence(AI), comprising: an electronic processing device; and a non-transitorycomputer-readable memory device storing instructions comprising (i) aclaim similarity AI model, (ii) a claim pricing AI model, and (iii) anAI chat module, wherein execution of the instructions by the electronicprocessing device results in: receiving, from a mobile application of amobile electronic device of an insured party and as an initiation of aprocess to submit a current insurance claim, an indication ofcomputer-readable data stored on a smart card; identifying, by theelectronic processing device and utilizing the data stored on the smartcard, a two-factor authentication challenge; transmitting, to the mobileelectronic device of the insured party and in response to theidentifying of the two-factor authentication challenge, the two-factorauthentication challenge; receiving, from the mobile electronic deviceof the insured party, an indication of a response to the two-factorauthentication challenge; authenticating, by the electronic processingdevice and utilizing the response to the two-factor authenticationchallenge, the insured party; converting, by an execution of the claimsimilarity AI model by the electronic processing device, the data storedon the smart card into a first number; receiving, from the mobileapplication of the mobile electronic device of the insured party, anindication of sensor data obtained by the mobile electronic device;converting, by an execution of the claim similarity AI model by theelectronic processing device, the sensor data into a second number;generating, by an execution of the AI chat module by the electronicprocessing device and based at least in part on the data stored on thesmart card, at least one chat query; receiving, from the mobileapplication of the mobile electronic device of the insured party and inresponse to the generating of the at least one chat query, an indicationof a chat response from the insured party; converting, by an executionof the claim similarity AI model by the electronic processing device,the chat response into a third number; comparing, by the electronicprocessing device, utilizing the first, second, and third numbers, andby accessing a database storing data descriptive of a plurality ofhistoric claims data, the current insurance claim to previous insuranceclaims; identifying, by the electronic processing device and based onthe comparing, at least one previous insurance claim that is numericallysimilar to the current insurance claim within a predefined confidencethreshold; computing, by an execution of the claim pricing AI model bythe electronic processing device and utilizing claim payment data storedwith respect to the at least one previous insurance claim, a resolutionfor the current insurance claim; and transmitting, to the mobileelectronic device of the insured party and in response to the computingof the resolution for the current insurance claim, a visual indicationof the resolution for the current insurance claim.
 16. The system forautomatically classifying multimedia claim data utilizing AI of claim15, wherein the instructions further comprise: (v) an AI fraud detectionmodel.
 17. The system for automatically classifying multimedia claimdata utilizing AI of claim 16, wherein the execution of the instructionsby the electronic processing device further results in: comparing, by anexecution of the AI fraud detection model by the electronic processingdevice, utilizing at least one of the first, second, and third numbers,and by accessing the database storing data descriptive of the pluralityof historic claims data, the current insurance claim to previousinsurance claims in which fraud had occurred; and computing, by theexecution of the AI fraud detection model by the electronic processingdevice and based on the comparing of the current insurance claim to theprevious insurance claims in which fraud had occurred, a statisticallikelihood that the current insurance claim is fraudulent.
 18. Thesystem for automatically classifying multimedia claim data utilizing AIof claim 15, wherein the instructions further comprise: (v) a cause ofloss AI model.
 19. The system for automatically classifying multimediaclaim data utilizing AI of claim 18, wherein the execution of theinstructions by the electronic processing device further results in:comparing, by an execution of the cause of loss AI model by theelectronic processing device, utilizing at least one of the first,second, and third numbers, and by accessing the database storing datadescriptive of the plurality of historic claims data, the currentinsurance claim to previous insurance claims in which a cause of losswas identified; and computing, by the execution of the cause of loss AImodel by the electronic processing device and based on the comparing ofthe current insurance claim to previous insurance claims in which acause of loss was identified, a cause of loss for the current insuranceclaim.
 20. The system for automatically classifying multimedia claimdata utilizing AI of claim 15, wherein the sensor data comprises atleast one of image and video data.